Providing Storage Tailored For A Storage Consuming Application

ABSTRACT

Providing storage tailored for a storage consuming application, including: identifying, for an application that utilizes storage resources within a cloud-based storage system, one or more storage performance characteristics associated with the application; comparing the storage performance characteristics of the application that were identified with storage performance characteristics of storage resources of one or more cloud-based storage systems; and selecting, based on the comparing, one or more storage resources within the one or more cloud-based storage systems to provide storage services to the application.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation application for patent entitled to a filing dateand claiming the benefit of earlier-filed U.S. Pat. No. 11,416,298,issued Aug. 16, 2022, herein incorporated by reference in its entirety,which claims priority from U.S. Provisional Patent Application No.62/701,239, filed Jul. 20, 2018; U.S. Provisional Patent Application No.62/731,295, filed Sep. 14, 2018; and U.S. Provisional Patent ApplicationNo. 62/875,947, filed Jul. 18, 2019.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A illustrates a first example system for data storage inaccordance with some implementations.

FIG. 1B illustrates a second example system for data storage inaccordance with some implementations.

FIG. 1C illustrates a third example system for data storage inaccordance with some implementations.

FIG. 1D illustrates a fourth example system for data storage inaccordance with some implementations.

FIG. 2A is a perspective view of a storage cluster with multiple storagenodes and internal storage coupled to each storage node to providenetwork attached storage, in accordance with some embodiments.

FIG. 2B is a block diagram showing an interconnect switch couplingmultiple storage nodes in accordance with some embodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode and contents of one of the non-volatile solid state storage unitsin accordance with some embodiments.

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes and storage units of some previous figures inaccordance with some embodiments.

FIG. 2E is a blade hardware block diagram, showing a control plane,compute and storage planes, and authorities interacting with underlyingphysical resources, in accordance with some embodiments.

FIG. 2F depicts elasticity software layers in blades of a storagecluster, in accordance with some embodiments.

FIG. 2G depicts authorities and storage resources in blades of a storagecluster, in accordance with some embodiments.

FIG. 3A sets forth a diagram of a storage system that is coupled fordata communications with a cloud services provider in accordance withsome embodiments of the present disclosure.

FIG. 3B sets forth a diagram of a storage system in accordance with someembodiments of the present disclosure.

FIG. 3C sets forth an example of a cloud-based storage system inaccordance with some embodiments of the present disclosure.

FIG. 3D sets forth an additional example of a cloud-based storage systemin accordance with some embodiments of the present disclosure.

FIG. 3E illustrates an exemplary computing device that may bespecifically configured to perform one or more of the processesdescribed herein.

FIG. 4 sets forth a flow chart illustrating an example method ofproviding application-specific storage by a storage system in accordancewith some embodiments of the present disclosure.

FIG. 5 sets forth a flow chart illustrating an additional example methodof providing application-specific storage by a storage system inaccordance with some embodiments of the present disclosure.

FIG. 6 sets forth a flow chart illustrating an additional example methodof providing application-specific storage by a storage system inaccordance with some embodiments of the present disclosure.

FIG. 7 sets forth a flow chart illustrating an additional example methodof providing application-specific storage by a storage system inaccordance with some embodiments of the present disclosure.

FIG. 8 sets forth a flow chart illustrating an additional example methodof providing application-specific storage by a storage system inaccordance with some embodiments of the present disclosure.

FIG. 9 sets forth a flow chart illustrating an additional example methodof providing application-specific storage by a storage system inaccordance with some embodiments of the present disclosure.

FIG. 10 sets forth a flow chart illustrating an example method ofproviding application-specific storage by a cloud-based storage systemin accordance with some embodiments of the present disclosure.

FIG. 11 sets forth a flow chart illustrating an additional examplemethod of providing application-specific storage by a cloud-basedstorage system in accordance with some embodiments of the presentdisclosure.

FIG. 12 sets forth a flow chart illustrating an additional examplemethod of providing application-specific storage by a cloud-basedstorage system in accordance with some embodiments of the presentdisclosure.

FIG. 13 sets forth a flow chart illustrating an additional examplemethod of providing application-specific storage by a cloud-basedstorage system in accordance with some embodiments of the presentdisclosure.

FIG. 14 sets forth a flow chart illustrating an additional examplemethod of providing application-specific storage by a cloud-basedstorage system in accordance with some embodiments of the presentdisclosure.

FIG. 15 sets forth a graphical user interface (‘GUI’) for tuning storageaccording to some embodiments of the present disclosure.

DESCRIPTION OF EMBODIMENTS

Example methods, apparatus, and products for providingapplication-specific storage by a storage system in accordance withembodiments of the present disclosure are described with reference tothe accompanying drawings, beginning with FIG. 1A. FIG. 1A illustratesan example system for data storage, in accordance with someimplementations. System 100 (also referred to as “storage system”herein) includes numerous elements for purposes of illustration ratherthan limitation. It may be noted that system 100 may include the same,more, or fewer elements configured in the same or different manner inother implementations.

System 100 includes a number of computing devices 164A-B. Computingdevices (also referred to as “client devices” herein) may be embodied,for example, a server in a data center, a workstation, a personalcomputer, a notebook, or the like. Computing devices 164A-B may becoupled for data communications to one or more storage arrays 102A-Bthrough a storage area network (‘SAN’) 158 or a local area network(‘LAN’) 160.

The SAN 158 may be implemented with a variety of data communicationsfabrics, devices, and protocols. For example, the fabrics for SAN 158may include Fibre Channel, Ethernet, Infiniband, Serial Attached SmallComputer System Interface (‘SAS’), or the like. Data communicationsprotocols for use with SAN 158 may include Advanced TechnologyAttachment (‘ATA’), Fibre Channel Protocol, Small Computer SystemInterface (‘SCSI’), Internet Small Computer System Interface (‘iSCSI’),HyperSCSI, Non-Volatile Memory Express (‘NVMe’) over Fabrics, or thelike. It may be noted that SAN 158 is provided for illustration, ratherthan limitation. Other data communication couplings may be implementedbetween computing devices 164A-B and storage arrays 102A-B.

The LAN 160 may also be implemented with a variety of fabrics, devices,and protocols. For example, the fabrics for LAN 160 may include Ethernet(802.3), wireless (802.11), or the like. Data communication protocolsfor use in LAN 160 may include Transmission Control Protocol (‘TCP’),User Datagram Protocol (‘UDP’), Internet Protocol (‘IP’), HyperTextTransfer Protocol (‘HTTP’), Wireless Access Protocol (‘WAP’), HandheldDevice Transport Protocol (‘HDTP’), Session Initiation Protocol (‘SIP’),Real Time Protocol (‘RTP’), or the like.

Storage arrays 102A-B may provide persistent data storage for thecomputing devices 164A-B. Storage array 102A may be contained in achassis (not shown), and storage array 102B may be contained in anotherchassis (not shown), in implementations. Storage array 102A and 102B mayinclude one or more storage array controllers 110A-D (also referred toas “controller” herein). A storage array controller 110A-D may beembodied as a module of automated computing machinery comprisingcomputer hardware, computer software, or a combination of computerhardware and software. In some implementations, the storage arraycontrollers 110A-D may be configured to carry out various storage tasks.Storage tasks may include writing data received from the computingdevices 164A-B to storage array 102A-B, erasing data from storage array102A-B, retrieving data from storage array 102A-B and providing data tocomputing devices 164A-B, monitoring and reporting of disk utilizationand performance, performing redundancy operations, such as RedundantArray of Independent Drives (‘RAID’) or RAID-like data redundancyoperations, compressing data, encrypting data, and so forth.

Storage array controller 110A-D may be implemented in a variety of ways,including as a Field Programmable Gate Array (‘FPGA’), a ProgrammableLogic Chip (‘PLC’), an Application Specific Integrated Circuit (‘ASIC’),System-on-Chip (‘SOC’), or any computing device that includes discretecomponents such as a processing device, central processing unit,computer memory, or various adapters. Storage array controller 110A-Dmay include, for example, a data communications adapter configured tosupport communications via the SAN 158 or LAN 160. In someimplementations, storage array controller 110A-D may be independentlycoupled to the LAN 160. In implementations, storage array controller110A-D may include an I/O controller or the like that couples thestorage array controller 110A-D for data communications, through amidplane (not shown), to a persistent storage resource 170A-B (alsoreferred to as a “storage resource” herein). The persistent storageresource 170A-B main include any number of storage drives 171A-F (alsoreferred to as “storage devices” herein) and any number of non-volatileRandom Access Memory (‘NVRAM’) devices (not shown).

In some implementations, the NVRAM devices of a persistent storageresource 170A-B may be configured to receive, from the storage arraycontroller 110A-D, data to be stored in the storage drives 171A-F. Insome examples, the data may originate from computing devices 164A-B. Insome examples, writing data to the NVRAM device may be carried out morequickly than directly writing data to the storage drive 171A-F. Inimplementations, the storage array controller 110A-D may be configuredto utilize the NVRAM devices as a quickly accessible buffer for datadestined to be written to the storage drives 171A-F. Latency for writerequests using NVRAM devices as a buffer may be improved relative to asystem in which a storage array controller 110A-D writes data directlyto the storage drives 171A-F. In some implementations, the NVRAM devicesmay be implemented with computer memory in the form of high bandwidth,low latency RAM. The NVRAM device is referred to as “non-volatile”because the NVRAM device may receive or include a unique power sourcethat maintains the state of the RAM after main power loss to the NVRAMdevice. Such a power source may be a battery, one or more capacitors, orthe like. In response to a power loss, the NVRAM device may beconfigured to write the contents of the RAM to a persistent storage,such as the storage drives 171A-F.

In implementations, storage drive 171A-F may refer to any deviceconfigured to record data persistently, where “persistently” or“persistent” refers as to a device's ability to maintain recorded dataafter loss of power. In some implementations, storage drive 171A-F maycorrespond to non-disk storage media. For example, the storage drive171A-F may be one or more solid-state drives (‘SSDs’), flash memorybased storage, any type of solid-state non-volatile memory, or any othertype of non-mechanical storage device. In other implementations, storagedrive 171A-F may include mechanical or spinning hard disk, such ashard-disk drives (‘HDD’).

In some implementations, the storage array controllers 110A-D may beconfigured for offloading device management responsibilities fromstorage drive 171A-F in storage array 102A-B. For example, storage arraycontrollers 110A-D may manage control information that may describe thestate of one or more memory blocks in the storage drives 171A-F. Thecontrol information may indicate, for example, that a particular memoryblock has failed and should no longer be written to, that a particularmemory block contains boot code for a storage array controller 110A-D,the number of program-erase (‘P/E’) cycles that have been performed on aparticular memory block, the age of data stored in a particular memoryblock, the type of data that is stored in a particular memory block, andso forth. In some implementations, the control information may be storedwith an associated memory block as metadata. In other implementations,the control information for the storage drives 171A-F may be stored inone or more particular memory blocks of the storage drives 171A-F thatare selected by the storage array controller 110A-D. The selected memoryblocks may be tagged with an identifier indicating that the selectedmemory block contains control information. The identifier may beutilized by the storage array controllers 110A-D in conjunction withstorage drives 171A-F to quickly identify the memory blocks that containcontrol information. For example, the storage controllers 110A-D mayissue a command to locate memory blocks that contain controlinformation. It may be noted that control information may be so largethat parts of the control information may be stored in multiplelocations, that the control information may be stored in multiplelocations for purposes of redundancy, for example, or that the controlinformation may otherwise be distributed across multiple memory blocksin the storage drive 171A-F.

In implementations, storage array controllers 110A-D may offload devicemanagement responsibilities from storage drives 171A-F of storage array102A-B by retrieving, from the storage drives 171A-F, controlinformation describing the state of one or more memory blocks in thestorage drives 171A-F. Retrieving the control information from thestorage drives 171A-F may be carried out, for example, by the storagearray controller 110A-D querying the storage drives 171A-F for thelocation of control information for a particular storage drive 171A-F.The storage drives 171A-F may be configured to execute instructions thatenable the storage drive 171A-F to identify the location of the controlinformation. The instructions may be executed by a controller (notshown) associated with or otherwise located on the storage drive 171A-Fand may cause the storage drive 171A-F to scan a portion of each memoryblock to identify the memory blocks that store control information forthe storage drives 171A-F. The storage drives 171A-F may respond bysending a response message to the storage array controller 110A-D thatincludes the location of control information for the storage drive171A-F. Responsive to receiving the response message, storage arraycontrollers 110A-D may issue a request to read data stored at theaddress associated with the location of control information for thestorage drives 171A-F.

In other implementations, the storage array controllers 110A-D mayfurther offload device management responsibilities from storage drives171A-F by performing, in response to receiving the control information,a storage drive management operation. A storage drive managementoperation may include, for example, an operation that is typicallyperformed by the storage drive 171A-F (e.g., the controller (not shown)associated with a particular storage drive 171A-F). A storage drivemanagement operation may include, for example, ensuring that data is notwritten to failed memory blocks within the storage drive 171A-F,ensuring that data is written to memory blocks within the storage drive171A-F in such a way that adequate wear leveling is achieved, and soforth.

In implementations, storage array 102A-B may implement two or morestorage array controllers 110A-D. For example, storage array 102A mayinclude storage array controllers 110A and storage array controllers110B. At a given instance, a single storage array controller 110A-D(e.g., storage array controller 110A) of a storage system 100 may bedesignated with primary status (also referred to as “primary controller”herein), and other storage array controllers 110A-D (e.g., storage arraycontroller 110A) may be designated with secondary status (also referredto as “secondary controller” herein). The primary controller may haveparticular rights, such as permission to alter data in persistentstorage resource 170A-B (e.g., writing data to persistent storageresource 170A-B). At least some of the rights of the primary controllermay supersede the rights of the secondary controller. For instance, thesecondary controller may not have permission to alter data in persistentstorage resource 170A-B when the primary controller has the right. Thestatus of storage array controllers 110A-D may change. For example,storage array controller 110A may be designated with secondary status,and storage array controller 110B may be designated with primary status.

In some implementations, a primary controller, such as storage arraycontroller 110A, may serve as the primary controller for one or morestorage arrays 102A-B, and a second controller, such as storage arraycontroller 110B, may serve as the secondary controller for the one ormore storage arrays 102A-B. For example, storage array controller 110Amay be the primary controller for storage array 102A and storage array102B, and storage array controller 110B may be the secondary controllerfor storage array 102A and 102B. In some implementations, storage arraycontrollers 110C and 110D (also referred to as “storage processingmodules”) may neither have primary or secondary status. Storage arraycontrollers 110C and 110D, implemented as storage processing modules,may act as a communication interface between the primary and secondarycontrollers (e.g., storage array controllers 110A and 110B,respectively) and storage array 102B. For example, storage arraycontroller 110A of storage array 102A may send a write request, via SAN158, to storage array 102B. The write request may be received by bothstorage array controllers 110C and 110D of storage array 102B. Storagearray controllers 110C and 110D facilitate the communication, e.g., sendthe write request to the appropriate storage drive 171A-F. It may benoted that in some implementations storage processing modules may beused to increase the number of storage drives controlled by the primaryand secondary controllers.

In implementations, storage array controllers 110A-D are communicativelycoupled, via a midplane (not shown), to one or more storage drives171A-F and to one or more NVRAM devices (not shown) that are included aspart of a storage array 102A-B. The storage array controllers 110A-D maybe coupled to the midplane via one or more data communication links andthe midplane may be coupled to the storage drives 171A-F and the NVRAMdevices via one or more data communications links. The datacommunications links described herein are collectively illustrated bydata communications links 108A-D and may include a Peripheral ComponentInterconnect Express (‘PCIe’) bus, for example.

FIG. 1B illustrates an example system for data storage, in accordancewith some implementations. Storage array controller 101 illustrated inFIG. 1B may be similar to the storage array controllers 110A-D describedwith respect to FIG. 1A. In one example, storage array controller 101may be similar to storage array controller 110A or storage arraycontroller 110B. Storage array controller 101 includes numerous elementsfor purposes of illustration rather than limitation. It may be notedthat storage array controller 101 may include the same, more, or fewerelements configured in the same or different manner in otherimplementations. It may be noted that elements of FIG. 1A may beincluded below to help illustrate features of storage array controller101.

Storage array controller 101 may include one or more processing devices104 and random access memory (‘RAM’) 111. Processing device 104 (orcontroller 101) represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 104 (or controller 101) may bea complex instruction set computing (‘CISC’) microprocessor, reducedinstruction set computing (‘RISC’) microprocessor, very long instructionword (‘VLIW’) microprocessor, or a processor implementing otherinstruction sets or processors implementing a combination of instructionsets. The processing device 104 (or controller 101) may also be one ormore special-purpose processing devices such as an application specificintegrated circuit (‘ASIC’), a field programmable gate array (‘FPGA’), adigital signal processor (‘DSP’), network processor, or the like.

The processing device 104 may be connected to the RAM 111 via a datacommunications link 106, which may be embodied as a high speed memorybus such as a Double-Data Rate 4 (‘DDR4’) bus. Stored in RAM 111 is anoperating system 112. In some implementations, instructions 113 arestored in RAM 111. Instructions 113 may include computer programinstructions for performing operations in in a direct-mapped flashstorage system. In one embodiment, a direct-mapped flash storage systemis one that that addresses data blocks within flash drives directly andwithout an address translation performed by the storage controllers ofthe flash drives.

In implementations, storage array controller 101 includes one or morehost bus adapters 103A-C that are coupled to the processing device 104via a data communications link 105A-C. In implementations, host busadapters 103A-C may be computer hardware that connects a host system(e.g., the storage array controller) to other network and storagearrays. In some examples, host bus adapters 103A-C may be a FibreChannel adapter that enables the storage array controller 101 to connectto a SAN, an Ethernet adapter that enables the storage array controller101 to connect to a LAN, or the like. Host bus adapters 103A-C may becoupled to the processing device 104 via a data communications link105A-C such as a PCIe bus.

In implementations, storage array controller 101 may include a host busadapter 114 that is coupled to an expander 115. The expander 115 may beused to attach a host system to a larger number of storage drives. Theexpander 115 may, for example, be a SAS expander utilized to enable thehost bus adapter 114 to attach to storage drives in an implementationwhere the host bus adapter 114 is embodied as a SAS controller.

In implementations, storage array controller 101 may include a switch116 coupled to the processing device 104 via a data communications link109. The switch 116 may be a computer hardware device that can createmultiple endpoints out of a single endpoint, thereby enabling multipledevices to share a single endpoint. The switch 116 may, for example, bea PCIe switch that is coupled to a PCIe bus (e.g., data communicationslink 109) and presents multiple PCIe connection points to the midplane.

In implementations, storage array controller 101 includes a datacommunications link 107 for coupling the storage array controller 101 toother storage array controllers. In some examples, data communicationslink 107 may be a QuickPath Interconnect (QPI) interconnect.

A traditional storage system that uses traditional flash drives mayimplement a process across the flash drives that are part of thetraditional storage system. For example, a higher level process of thestorage system may initiate and control a process across the flashdrives. However, a flash drive of the traditional storage system mayinclude its own storage controller that also performs the process. Thus,for the traditional storage system, a higher level process (e.g.,initiated by the storage system) and a lower level process (e.g.,initiated by a storage controller of the storage system) may both beperformed.

To resolve various deficiencies of a traditional storage system,operations may be performed by higher level processes and not by thelower level processes. For example, the flash storage system may includeflash drives that do not include storage controllers that provide theprocess. Thus, the operating system of the flash storage system itselfmay initiate and control the process. This may be accomplished by adirect-mapped flash storage system that addresses data blocks within theflash drives directly and without an address translation performed bythe storage controllers of the flash drives.

The operating system of the flash storage system may identify andmaintain a list of allocation units across multiple flash drives of theflash storage system. The allocation units may be entire erase blocks ormultiple erase blocks. The operating system may maintain a map oraddress range that directly maps addresses to erase blocks of the flashdrives of the flash storage system.

Direct mapping to the erase blocks of the flash drives may be used torewrite data and erase data. For example, the operations may beperformed on one or more allocation units that include a first data anda second data where the first data is to be retained and the second datais no longer being used by the flash storage system. The operatingsystem may initiate the process to write the first data to new locationswithin other allocation units and erasing the second data and markingthe allocation units as being available for use for subsequent data.Thus, the process may only be performed by the higher level operatingsystem of the flash storage system without an additional lower levelprocess being performed by controllers of the flash drives.

Advantages of the process being performed only by the operating systemof the flash storage system include increased reliability of the flashdrives of the flash storage system as unnecessary or redundant writeoperations are not being performed during the process. One possiblepoint of novelty here is the concept of initiating and controlling theprocess at the operating system of the flash storage system. Inaddition, the process can be controlled by the operating system acrossmultiple flash drives. This is contrast to the process being performedby a storage controller of a flash drive.

A storage system can consist of two storage array controllers that sharea set of drives for failover purposes, or it could consist of a singlestorage array controller that provides a storage service that utilizesmultiple drives, or it could consist of a distributed network of storagearray controllers each with some number of drives or some amount ofFlash storage where the storage array controllers in the networkcollaborate to provide a complete storage service and collaborate onvarious aspects of a storage service including storage allocation andgarbage collection.

FIG. 1C illustrates a third example system 117 for data storage inaccordance with some implementations. System 117 (also referred to as“storage system” herein) includes numerous elements for purposes ofillustration rather than limitation. It may be noted that system 117 mayinclude the same, more, or fewer elements configured in the same ordifferent manner in other implementations.

In one embodiment, system 117 includes a dual Peripheral ComponentInterconnect (PCP) flash storage device 118 with separately addressablefast write storage. System 117 may include a storage controller 119. Inone embodiment, storage controller 119A-D may be a CPU, ASIC, FPGA, orany other circuitry that may implement control structures necessaryaccording to the present disclosure. In one embodiment, system 117includes flash memory devices (e.g., including flash memory devices 120a-n), operatively coupled to various channels of the storage devicecontroller 119. Flash memory devices 120 a-n, may be presented to thecontroller 119A-D as an addressable collection of Flash pages, eraseblocks, and/or control elements sufficient to allow the storage devicecontroller 119A-D to program and retrieve various aspects of the Flash.In one embodiment, storage device controller 119A-D may performoperations on flash memory devices 120 a-n including storing andretrieving data content of pages, arranging and erasing any blocks,tracking statistics related to the use and reuse of Flash memory pages,erase blocks, and cells, tracking and predicting error codes and faultswithin the Flash memory, controlling voltage levels associated withprogramming and retrieving contents of Flash cells, etc.

In one embodiment, system 117 may include RAM 121 to store separatelyaddressable fast-write data. In one embodiment, RAM 121 may be one ormore separate discrete devices. In another embodiment, RAM 121 may beintegrated into storage device controller 119A-D or multiple storagedevice controllers. The RAM 121 may be utilized for other purposes aswell, such as temporary program memory for a processing device (e.g., aCPU) in the storage device controller 119.

In one embodiment, system 117 may include a stored energy device 122,such as a rechargeable battery or a capacitor. Stored energy device 122may store energy sufficient to power the storage device controller 119,some amount of the RAM (e.g., RAM 121), and some amount of Flash memory(e.g., Flash memory 120 a-120 n) for sufficient time to write thecontents of RAM to Flash memory. In one embodiment, storage devicecontroller 119A-D may write the contents of RAM to Flash Memory if thestorage device controller detects loss of external power.

In one embodiment, system 117 includes two data communications links 123a, 123 b. In one embodiment, data communications links 123 a, 123 b maybe PCI interfaces. In another embodiment, data communications links 123a, 123 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Data communications links 123 a, 123b may be based on non-volatile memory express (‘NVMe’) or NVMe overfabrics (‘NVMf’) specifications that allow external connection to thestorage device controller 119A-D from other components in the storagesystem 117. It should be noted that data communications links may beinterchangeably referred to herein as PCI buses for convenience.

System 117 may also include an external power source (not shown), whichmay be provided over one or both data communications links 123 a, 123 b,or which may be provided separately. An alternative embodiment includesa separate Flash memory (not shown) dedicated for use in storing thecontent of RAM 121. The storage device controller 119A-D may present alogical device over a PCI bus which may include an addressablefast-write logical device, or a distinct part of the logical addressspace of the storage device 118, which may be presented as PCI memory oras persistent storage. In one embodiment, operations to store into thedevice are directed into the RAM 121. On power failure, the storagedevice controller 119A-D may write stored content associated with theaddressable fast-write logical storage to Flash memory (e.g., Flashmemory 120 a-n) for long-term persistent storage.

In one embodiment, the logical device may include some presentation ofsome or all of the content of the Flash memory devices 120 a-n, wherethat presentation allows a storage system including a storage device 118(e.g., storage system 117) to directly address Flash memory pages anddirectly reprogram erase blocks from storage system components that areexternal to the storage device through the PCI bus. The presentation mayalso allow one or more of the external components to control andretrieve other aspects of the Flash memory including some or all of:tracking statistics related to use and reuse of Flash memory pages,erase blocks, and cells across all the Flash memory devices; trackingand predicting error codes and faults within and across the Flash memorydevices; controlling voltage levels associated with programming andretrieving contents of Flash cells; etc.

In one embodiment, the stored energy device 122 may be sufficient toensure completion of in-progress operations to the Flash memory devices120 a-120 n stored energy device 122 may power storage device controller119A-D and associated Flash memory devices (e.g., 120 a-n) for thoseoperations, as well as for the storing of fast-write RAM to Flashmemory. Stored energy device 122 may be used to store accumulatedstatistics and other parameters kept and tracked by the Flash memorydevices 120 a-n and/or the storage device controller 119. Separatecapacitors or stored energy devices (such as smaller capacitors near orembedded within the Flash memory devices themselves) may be used forsome or all of the operations described herein.

Various schemes may be used to track and optimize the life span of thestored energy component, such as adjusting voltage levels over time,partially discharging the storage energy device 122 to measurecorresponding discharge characteristics, etc. If the available energydecreases over time, the effective available capacity of the addressablefast-write storage may be decreased to ensure that it can be writtensafely based on the currently available stored energy.

FIG. 1D illustrates a third example system 124 for data storage inaccordance with some implementations. In one embodiment, system 124includes storage controllers 125 a, 125 b. In one embodiment, storagecontrollers 125 a, 125 b are operatively coupled to Dual PCI storagedevices 119 a, 119 b and 119 c, 119 d, respectively. Storage controllers125 a, 125 b may be operatively coupled (e.g., via a storage network130) to some number of host computers 127 a-n.

In one embodiment, two storage controllers (e.g., 125 a and 125 b)provide storage services, such as a SCS) block storage array, a fileserver, an object server, a database or data analytics service, etc. Thestorage controllers 125 a, 125 b may provide services through somenumber of network interfaces (e.g., 126 a-d) to host computers 127 a-noutside of the storage system 124. Storage controllers 125 a, 125 b mayprovide integrated services or an application entirely within thestorage system 124, forming a converged storage and compute system. Thestorage controllers 125 a, 125 b may utilize the fast write memorywithin or across storage devices 119 a-d to journal in progressoperations to ensure the operations are not lost on a power failure,storage controller removal, storage controller or storage systemshutdown, or some fault of one or more software or hardware componentswithin the storage system 124.

In one embodiment, controllers 125 a, 125 b operate as PCI masters toone or the other PCI buses 128 a, 128 b. In another embodiment, 128 aand 128 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Other storage system embodiments mayoperate storage controllers 125 a, 125 b as multi-masters for both PCIbuses 128 a, 128 b. Alternately, a PCI/NVMe/NVMf switchinginfrastructure or fabric may connect multiple storage controllers. Somestorage system embodiments may allow storage devices to communicate witheach other directly rather than communicating only with storagecontrollers. In one embodiment, a storage device controller 119 a may beoperable under direction from a storage controller 125 a to synthesizeand transfer data to be stored into Flash memory devices from data thathas been stored in RAM (e.g., RAM 121 of FIG. 1C). For example, arecalculated version of RAM content may be transferred after a storagecontroller has determined that an operation has fully committed acrossthe storage system, or when fast-write memory on the device has reacheda certain used capacity, or after a certain amount of time, to ensureimprove safety of the data or to release addressable fast-write capacityfor reuse. This mechanism may be used, for example, to avoid a secondtransfer over a bus (e.g., 128 a, 128 b) from the storage controllers125 a, 125 b. In one embodiment, a recalculation may include compressingdata, attaching indexing or other metadata, combining multiple datasegments together, performing erasure code calculations, etc.

In one embodiment, under direction from a storage controller 125 a, 125b, a storage device controller 119 a, 119 b may be operable to calculateand transfer data to other storage devices from data stored in RAM(e.g., RAM 121 of FIG. 1C) without involvement of the storagecontrollers 125 a, 125 b. This operation may be used to mirror datastored in one controller 125 a to another controller 125 b, or it couldbe used to offload compression, data aggregation, and/or erasure codingcalculations and transfers to storage devices to reduce load on storagecontrollers or the storage controller interface 129 a, 129 b to the PCIbus 128 a, 128 b.

A storage device controller 119A-D may include mechanisms forimplementing high availability primitives for use by other parts of astorage system external to the Dual PCI storage device 118. For example,reservation or exclusion primitives may be provided so that, in astorage system with two storage controllers providing a highly availablestorage service, one storage controller may prevent the other storagecontroller from accessing or continuing to access the storage device.This could be used, for example, in cases where one controller detectsthat the other controller is not functioning properly or where theinterconnect between the two storage controllers may itself not befunctioning properly.

In one embodiment, a storage system for use with Dual PCI direct mappedstorage devices with separately addressable fast write storage includessystems that manage erase blocks or groups of erase blocks as allocationunits for storing data on behalf of the storage service, or for storingmetadata (e.g., indexes, logs, etc.) associated with the storageservice, or for proper management of the storage system itself. Flashpages, which may be a few kilobytes in size, may be written as dataarrives or as the storage system is to persist data for long intervalsof time (e.g., above a defined threshold of time). To commit data morequickly, or to reduce the number of writes to the Flash memory devices,the storage controllers may first write data into the separatelyaddressable fast write storage on one more storage devices.

In one embodiment, the storage controllers 125 a, 125 b may initiate theuse of erase blocks within and across storage devices (e.g., 118) inaccordance with an age and expected remaining lifespan of the storagedevices, or based on other statistics. The storage controllers 125 a,125 b may initiate garbage collection and data migration data betweenstorage devices in accordance with pages that are no longer needed aswell as to manage Flash page and erase block lifespans and to manageoverall system performance.

In one embodiment, the storage system 124 may utilize mirroring and/orerasure coding schemes as part of storing data into addressable fastwrite storage and/or as part of writing data into allocation unitsassociated with erase blocks. Erasure codes may be used across storagedevices, as well as within erase blocks or allocation units, or withinand across Flash memory devices on a single storage device, to provideredundancy against single or multiple storage device failures or toprotect against internal corruptions of Flash memory pages resultingfrom Flash memory operations or from degradation of Flash memory cells.Mirroring and erasure coding at various levels may be used to recoverfrom multiple types of failures that occur separately or in combination.

The embodiments depicted with reference to FIGS. 2A-G illustrate astorage cluster that stores user data, such as user data originatingfrom one or more user or client systems or other sources external to thestorage cluster. The storage cluster distributes user data acrossstorage nodes housed within a chassis, or across multiple chassis, usingerasure coding and redundant copies of metadata. Erasure coding refersto a method of data protection or reconstruction in which data is storedacross a set of different locations, such as disks, storage nodes orgeographic locations. Flash memory is one type of solid-state memorythat may be integrated with the embodiments, although the embodimentsmay be extended to other types of solid-state memory or other storagemedium, including non-solid state memory. Control of storage locationsand workloads are distributed across the storage locations in aclustered peer-to-peer system. Tasks such as mediating communicationsbetween the various storage nodes, detecting when a storage node hasbecome unavailable, and balancing I/Os (inputs and outputs) across thevarious storage nodes, are all handled on a distributed basis. Data islaid out or distributed across multiple storage nodes in data fragmentsor stripes that support data recovery in some embodiments. Ownership ofdata can be reassigned within a cluster, independent of input and outputpatterns. This architecture described in more detail below allows astorage node in the cluster to fail, with the system remainingoperational, since the data can be reconstructed from other storagenodes and thus remain available for input and output operations. Invarious embodiments, a storage node may be referred to as a clusternode, a blade, or a server.

The storage cluster may be contained within a chassis, i.e. an enclosurehousing one or more storage nodes. A mechanism to provide power to eachstorage node, such as a power distribution bus, and a communicationmechanism, such as a communication bus that enables communicationbetween the storage nodes are included within the chassis. The storagecluster can run as an independent system in one location according tosome embodiments. In one embodiment, a chassis contains at least twoinstances of both the power distribution and the communication bus whichmay be enabled or disabled independently. The internal communication busmay be an Ethernet bus, however, other technologies such as PCIe,InfiniBand, and others, are equally suitable. The chassis provides aport for an external communication bus for enabling communicationbetween multiple chassis, directly or through a switch, and with clientsystems. The external communication may use a technology such asEthernet, InfiniBand, Fibre Channel, etc. In some embodiments, theexternal communication bus uses different communication bus technologiesfor inter-chassis and client communication. If a switch is deployedwithin or between chassis, the switch may act as a translation betweenmultiple protocols or technologies. When multiple chassis are connectedto define a storage cluster, the storage cluster may be accessed by aclient using either proprietary interfaces or standard interfaces suchas network file system (‘NFS’), common internet file system (‘CIFS’),small computer system interface (‘SCSI’) or hypertext transfer protocol(‘HTTP’). Translation from the client protocol may occur at the switch,chassis external communication bus or within each storage node. In someembodiments, multiple chassis may be coupled or connected to each otherthrough an aggregator switch. A portion and/or all of the coupled orconnected chassis may be designated as a storage cluster. As discussedabove, each chassis can have multiple blades, each blade has a mediaaccess control (‘MAC’) address, but the storage cluster is presented toan external network as having a single cluster IP address and a singleMAC address in some embodiments.

Each storage node may be one or more storage servers and each storageserver is connected to one or more non-volatile solid state memoryunits, which may be referred to as storage units or storage devices. Oneembodiment includes a single storage server in each storage node andbetween one to eight non-volatile solid state memory units, however thisone example is not meant to be limiting. The storage server may includea processor, DRAM and interfaces for the internal communication bus andpower distribution for each of the power buses. Inside the storage node,the interfaces and storage unit share a communication bus, e.g., PCIExpress, in some embodiments. The non-volatile solid state memory unitsmay directly access the internal communication bus interface through astorage node communication bus, or request the storage node to accessthe bus interface. The non-volatile solid state memory unit contains anembedded CPU, solid state storage controller, and a quantity of solidstate mass storage, e.g., between 2-32 terabytes (‘TB’) in someembodiments. An embedded volatile storage medium, such as DRAM, and anenergy reserve apparatus are included in the non-volatile solid statememory unit. In some embodiments, the energy reserve apparatus is acapacitor, super-capacitor, or battery that enables transferring asubset of DRAM contents to a stable storage medium in the case of powerloss. In some embodiments, the non-volatile solid state memory unit isconstructed with a storage class memory, such as phase change ormagnetoresistive random access memory (‘MRAM’) that substitutes for DRAMand enables a reduced power hold-up apparatus.

One of many features of the storage nodes and non-volatile solid statestorage is the ability to proactively rebuild data in a storage cluster.The storage nodes and non-volatile solid state storage can determinewhen a storage node or non-volatile solid state storage in the storagecluster is unreachable, independent of whether there is an attempt toread data involving that storage node or non-volatile solid statestorage. The storage nodes and non-volatile solid state storage thencooperate to recover and rebuild the data in at least partially newlocations. This constitutes a proactive rebuild, in that the systemrebuilds data without waiting until the data is needed for a read accessinitiated from a client system employing the storage cluster. These andfurther details of the storage memory and operation thereof arediscussed below.

FIG. 2A is a perspective view of a storage cluster 161, with multiplestorage nodes 150 and internal solid-state memory coupled to eachstorage node to provide network attached storage or storage areanetwork, in accordance with some embodiments. A network attachedstorage, storage area network, or a storage cluster, or other storagememory, could include one or more storage clusters 161, each having oneor more storage nodes 150, in a flexible and reconfigurable arrangementof both the physical components and the amount of storage memoryprovided thereby. The storage cluster 161 is designed to fit in a rack,and one or more racks can be set up and populated as desired for thestorage memory. The storage cluster 161 has a chassis 138 havingmultiple slots 142. It should be appreciated that chassis 138 may bereferred to as a housing, enclosure, or rack unit. In one embodiment,the chassis 138 has fourteen slots 142, although other numbers of slotsare readily devised. For example, some embodiments have four slots,eight slots, sixteen slots, thirty-two slots, or other suitable numberof slots. Each slot 142 can accommodate one storage node 150 in someembodiments. Chassis 138 includes flaps 148 that can be utilized tomount the chassis 138 on a rack. Fans 144 provide air circulation forcooling of the storage nodes 150 and components thereof, although othercooling components could be used, or an embodiment could be devisedwithout cooling components. A switch fabric 146 couples storage nodes150 within chassis 138 together and to a network for communication tothe memory. In an embodiment depicted in herein, the slots 142 to theleft of the switch fabric 146 and fans 144 are shown occupied by storagenodes 150, while the slots 142 to the right of the switch fabric 146 andfans 144 are empty and available for insertion of storage node 150 forillustrative purposes. This configuration is one example, and one ormore storage nodes 150 could occupy the slots 142 in various furtherarrangements. The storage node arrangements need not be sequential oradjacent in some embodiments. Storage nodes 150 are hot pluggable,meaning that a storage node 150 can be inserted into a slot 142 in thechassis 138, or removed from a slot 142, without stopping or poweringdown the system. Upon insertion or removal of storage node 150 from slot142, the system automatically reconfigures in order to recognize andadapt to the change. Reconfiguration, in some embodiments, includesrestoring redundancy and/or rebalancing data or load.

Each storage node 150 can have multiple components. In the embodimentshown here, the storage node 150 includes a printed circuit board 159populated by a CPU 156, i.e. processor, a memory 154 coupled to the CPU156, and a non-volatile solid state storage 152 coupled to the CPU 156,although other mountings and/or components could be used in furtherembodiments. The memory 154 has instructions which are executed by theCPU 156 and/or data operated on by the CPU 156. As further explainedbelow, the non-volatile solid state storage 152 includes flash or, infurther embodiments, other types of solid-state memory.

Referring to FIG. 2A, storage cluster 161 is scalable, meaning thatstorage capacity with non-uniform storage sizes is readily added, asdescribed above. One or more storage nodes 150 can be plugged into orremoved from each chassis and the storage cluster self-configures insome embodiments. Plug-in storage nodes 150, whether installed in achassis as delivered or later added, can have different sizes. Forexample, in one embodiment a storage node 150 can have any multiple of 4TB, e.g., 8 TB, 12 TB, 16 TB, 32 TB, etc. In further embodiments, astorage node 150 could have any multiple of other storage amounts orcapacities. Storage capacity of each storage node 150 is broadcast, andinfluences decisions of how to stripe the data. For maximum storageefficiency, an embodiment can self-configure as wide as possible in thestripe, subject to a predetermined requirement of continued operationwith loss of up to one, or up to two, non-volatile solid state storageunits 152 or storage nodes 150 within the chassis.

FIG. 2B is a block diagram showing a communications interconnect 173 andpower distribution bus 172 coupling multiple storage nodes 150.Referring back to FIG. 2A, the communications interconnect 173 can beincluded in or implemented with the switch fabric 146 in someembodiments. Where multiple storage clusters 161 occupy a rack, thecommunications interconnect 173 can be included in or implemented with atop of rack switch, in some embodiments. As illustrated in FIG. 2B,storage cluster 161 is enclosed within a single chassis 138. Externalport 176 is coupled to storage nodes 150 through communicationsinterconnect 173, while external port 174 is coupled directly to astorage node. External power port 178 is coupled to power distributionbus 172. Storage nodes 150 may include varying amounts and differingcapacities of non-volatile solid state storage 152 as described withreference to FIG. 2A. In addition, one or more storage nodes 150 may bea compute only storage node as illustrated in FIG. 2B. Authorities 168are implemented on the non-volatile solid state storages 152, forexample as lists or other data structures stored in memory. In someembodiments the authorities are stored within the non-volatile solidstate storage 152 and supported by software executing on a controller orother processor of the non-volatile solid state storage 152. In afurther embodiment, authorities 168 are implemented on the storage nodes150, for example as lists or other data structures stored in the memory154 and supported by software executing on the CPU 156 of the storagenode 150. Authorities 168 control how and where data is stored in thenon-volatile solid state storages 152 in some embodiments. This controlassists in determining which type of erasure coding scheme is applied tothe data, and which storage nodes 150 have which portions of the data.Each authority 168 may be assigned to a non-volatile solid state storage152. Each authority may control a range of inode numbers, segmentnumbers, or other data identifiers which are assigned to data by a filesystem, by the storage nodes 150, or by the non-volatile solid statestorage 152, in various embodiments.

Every piece of data, and every piece of metadata, has redundancy in thesystem in some embodiments. In addition, every piece of data and everypiece of metadata has an owner, which may be referred to as anauthority. If that authority is unreachable, for example through failureof a storage node, there is a plan of succession for how to find thatdata or that metadata. In various embodiments, there are redundantcopies of authorities 168. Authorities 168 have a relationship tostorage nodes 150 and non-volatile solid state storage 152 in someembodiments. Each authority 168, covering a range of data segmentnumbers or other identifiers of the data, may be assigned to a specificnon-volatile solid state storage 152. In some embodiments theauthorities 168 for all of such ranges are distributed over thenon-volatile solid state storages 152 of a storage cluster. Each storagenode 150 has a network port that provides access to the non-volatilesolid state storage(s) 152 of that storage node 150. Data can be storedin a segment, which is associated with a segment number and that segmentnumber is an indirection for a configuration of a RAID (redundant arrayof independent disks) stripe in some embodiments. The assignment and useof the authorities 168 thus establishes an indirection to data.Indirection may be referred to as the ability to reference dataindirectly, in this case via an authority 168, in accordance with someembodiments. A segment identifies a set of non-volatile solid statestorage 152 and a local identifier into the set of non-volatile solidstate storage 152 that may contain data. In some embodiments, the localidentifier is an offset into the device and may be reused sequentiallyby multiple segments. In some embodiments the local identifier is uniquefor a specific segment and never reused. The offsets in the non-volatilesolid state storage 152 are applied to locating data for writing to orreading from the non-volatile solid state storage 152 (in the form of aRAID stripe). Data is striped across multiple units of non-volatilesolid state storage 152, which may include or be different from thenon-volatile solid state storage 152 having the authority 168 for aparticular data segment.

If there is a change in where a particular segment of data is located,e.g., during a data move or a data reconstruction, the authority 168 forthat data segment should be consulted, at that non-volatile solid statestorage 152 or storage node 150 having that authority 168. In order tolocate a particular piece of data, embodiments calculate a hash valuefor a data segment or apply an inode number or a data segment number.The output of this operation points to a non-volatile solid statestorage 152 having the authority 168 for that particular piece of data.In some embodiments there are two stages to this operation. The firststage maps an entity identifier (ID), e.g., a segment number, inodenumber, or directory number to an authority identifier. This mapping mayinclude a calculation such as a hash or a bit mask. The second stage ismapping the authority identifier to a particular non-volatile solidstate storage 152, which may be done through an explicit mapping. Theoperation is repeatable, so that when the calculation is performed, theresult of the calculation repeatably and reliably points to a particularnon-volatile solid state storage 152 having that authority 168. Theoperation may include the set of reachable storage nodes as input. Ifthe set of reachable non-volatile solid state storage units changes theoptimal set changes. In some embodiments, the persisted value is thecurrent assignment (which is always true) and the calculated value isthe target assignment the cluster will attempt to reconfigure towards.This calculation may be used to determine the optimal non-volatile solidstate storage 152 for an authority in the presence of a set ofnon-volatile solid state storage 152 that are reachable and constitutethe same cluster. The calculation also determines an ordered set of peernon-volatile solid state storage 152 that will also record the authorityto non-volatile solid state storage mapping so that the authority may bedetermined even if the assigned non-volatile solid state storage isunreachable. A duplicate or substitute authority 168 may be consulted ifa specific authority 168 is unavailable in some embodiments.

With reference to FIGS. 2A and 2B, two of the many tasks of the CPU 156on a storage node 150 are to break up write data, and reassemble readdata. When the system has determined that data is to be written, theauthority 168 for that data is located as above. When the segment ID fordata is already determined the request to write is forwarded to thenon-volatile solid state storage 152 currently determined to be the hostof the authority 168 determined from the segment. The host CPU 156 ofthe storage node 150, on which the non-volatile solid state storage 152and corresponding authority 168 reside, then breaks up or shards thedata and transmits the data out to various non-volatile solid statestorage 152. The transmitted data is written as a data stripe inaccordance with an erasure coding scheme. In some embodiments, data isrequested to be pulled, and in some embodiments, data is pushed. Inreverse, when data is read, the authority 168 for the segment IDcontaining the data is located as described above. The host CPU 156 ofthe storage node 150 on which the non-volatile solid state storage 152and corresponding authority 168 reside requests the data from thenon-volatile solid state storage and corresponding storage nodes pointedto by the authority. In some embodiments the data is read from flashstorage as a data stripe. The host CPU 156 of storage node 150 thenreassembles the read data, correcting any errors (if present) accordingto the appropriate erasure coding scheme, and forwards the reassembleddata to the network. In further embodiments, some or all of these taskscan be handled in the non-volatile solid state storage 152. In someembodiments, the segment host requests the data be sent to storage node150 by requesting pages from storage and then sending the data to thestorage node making the original request.

In some systems, for example in UNIX-style file systems, data is handledwith an index node or inode, which specifies a data structure thatrepresents an object in a file system. The object could be a file or adirectory, for example. Metadata may accompany the object, as attributessuch as permission data and a creation timestamp, among otherattributes. A segment number could be assigned to all or a portion ofsuch an object in a file system. In other systems, data segments arehandled with a segment number assigned elsewhere. For purposes ofdiscussion, the unit of distribution is an entity, and an entity can bea file, a directory or a segment. That is, entities are units of data ormetadata stored by a storage system. Entities are grouped into setscalled authorities. Each authority has an authority owner, which is astorage node that has the exclusive right to update the entities in theauthority. In other words, a storage node contains the authority, andthat the authority, in turn, contains entities.

A segment is a logical container of data in accordance with someembodiments. A segment is an address space between medium address spaceand physical flash locations, i.e. the data segment number, are in thisaddress space. Segments may also contain meta-data, which enable dataredundancy to be restored (rewritten to different flash locations ordevices) without the involvement of higher level software. In oneembodiment, an internal format of a segment contains client data andmedium mappings to determine the position of that data. Each datasegment is protected, e.g., from memory and other failures, by breakingthe segment into a number of data and parity shards, where applicable.The data and parity shards are distributed, i.e. striped, acrossnon-volatile solid state storage 152 coupled to the host CPUs 156 (SeeFIGS. 2E and 2G) in accordance with an erasure coding scheme. Usage ofthe term segments refers to the container and its place in the addressspace of segments in some embodiments. Usage of the term stripe refersto the same set of shards as a segment and includes how the shards aredistributed along with redundancy or parity information in accordancewith some embodiments.

A series of address-space transformations takes place across an entirestorage system. At the top are the directory entries (file names) whichlink to an inode. Inodes point into medium address space, where data islogically stored. Medium addresses may be mapped through a series ofindirect mediums to spread the load of large files, or implement dataservices like deduplication or snapshots. Medium addresses may be mappedthrough a series of indirect mediums to spread the load of large files,or implement data services like deduplication or snapshots. Segmentaddresses are then translated into physical flash locations. Physicalflash locations have an address range bounded by the amount of flash inthe system in accordance with some embodiments. Medium addresses andsegment addresses are logical containers, and in some embodiments use a128 bit or larger identifier so as to be practically infinite, with alikelihood of reuse calculated as longer than the expected life of thesystem. Addresses from logical containers are allocated in ahierarchical fashion in some embodiments. Initially, each non-volatilesolid state storage unit 152 may be assigned a range of address space.Within this assigned range, the non-volatile solid state storage 152 isable to allocate addresses without synchronization with othernon-volatile solid state storage 152.

Data and metadata is stored by a set of underlying storage layouts thatare optimized for varying workload patterns and storage devices. Theselayouts incorporate multiple redundancy schemes, compression formats andindex algorithms. Some of these layouts store information aboutauthorities and authority masters, while others store file metadata andfile data. The redundancy schemes include error correction codes thattolerate corrupted bits within a single storage device (such as a NANDflash chip), erasure codes that tolerate the failure of multiple storagenodes, and replication schemes that tolerate data center or regionalfailures. In some embodiments, low density parity check (‘LDPC’) code isused within a single storage unit. Reed-Solomon encoding is used withina storage cluster, and mirroring is used within a storage grid in someembodiments. Metadata may be stored using an ordered log structuredindex (such as a Log Structured Merge Tree), and large data may not bestored in a log structured layout.

In order to maintain consistency across multiple copies of an entity,the storage nodes agree implicitly on two things through calculations:(1) the authority that contains the entity, and (2) the storage nodethat contains the authority. The assignment of entities to authoritiescan be done by pseudo randomly assigning entities to authorities, bysplitting entities into ranges based upon an externally produced key, orby placing a single entity into each authority. Examples of pseudorandomschemes are linear hashing and the Replication Under Scalable Hashing(‘RUSH’) family of hashes, including Controlled Replication UnderScalable Hashing (‘CRUSH’). In some embodiments, pseudo-randomassignment is utilized only for assigning authorities to nodes becausethe set of nodes can change. The set of authorities cannot change so anysubjective function may be applied in these embodiments. Some placementschemes automatically place authorities on storage nodes, while otherplacement schemes rely on an explicit mapping of authorities to storagenodes. In some embodiments, a pseudorandom scheme is utilized to mapfrom each authority to a set of candidate authority owners. Apseudorandom data distribution function related to CRUSH may assignauthorities to storage nodes and create a list of where the authoritiesare assigned. Each storage node has a copy of the pseudorandom datadistribution function, and can arrive at the same calculation fordistributing, and later finding or locating an authority. Each of thepseudorandom schemes requires the reachable set of storage nodes asinput in some embodiments in order to conclude the same target nodes.Once an entity has been placed in an authority, the entity may be storedon physical devices so that no expected failure will lead to unexpecteddata loss. In some embodiments, rebalancing algorithms attempt to storethe copies of all entities within an authority in the same layout and onthe same set of machines.

Examples of expected failures include device failures, stolen machines,datacenter fires, and regional disasters, such as nuclear or geologicalevents. Different failures lead to different levels of acceptable dataloss. In some embodiments, a stolen storage node impacts neither thesecurity nor the reliability of the system, while depending on systemconfiguration, a regional event could lead to no loss of data, a fewseconds or minutes of lost updates, or even complete data loss.

In the embodiments, the placement of data for storage redundancy isindependent of the placement of authorities for data consistency. Insome embodiments, storage nodes that contain authorities do not containany persistent storage. Instead, the storage nodes are connected tonon-volatile solid state storage units that do not contain authorities.The communications interconnect between storage nodes and non-volatilesolid state storage units consists of multiple communicationtechnologies and has non-uniform performance and fault tolerancecharacteristics. In some embodiments, as mentioned above, non-volatilesolid state storage units are connected to storage nodes via PCIexpress, storage nodes are connected together within a single chassisusing Ethernet backplane, and chassis are connected together to form astorage cluster. Storage clusters are connected to clients usingEthernet or fiber channel in some embodiments. If multiple storageclusters are configured into a storage grid, the multiple storageclusters are connected using the Internet or other long-distancenetworking links, such as a “metro scale” link or private link that doesnot traverse the internet.

Authority owners have the exclusive right to modify entities, to migrateentities from one non-volatile solid state storage unit to anothernon-volatile solid state storage unit, and to add and remove copies ofentities. This allows for maintaining the redundancy of the underlyingdata. When an authority owner fails, is going to be decommissioned, oris overloaded, the authority is transferred to a new storage node.Transient failures make it non-trivial to ensure that all non-faultymachines agree upon the new authority location. The ambiguity thatarises due to transient failures can be achieved automatically by aconsensus protocol such as Paxos, hot-warm failover schemes, via manualintervention by a remote system administrator, or by a local hardwareadministrator (such as by physically removing the failed machine fromthe cluster, or pressing a button on the failed machine). In someembodiments, a consensus protocol is used, and failover is automatic. Iftoo many failures or replication events occur in too short a timeperiod, the system goes into a self-preservation mode and haltsreplication and data movement activities until an administratorintervenes in accordance with some embodiments.

As authorities are transferred between storage nodes and authorityowners update entities in their authorities, the system transfersmessages between the storage nodes and non-volatile solid state storageunits. With regard to persistent messages, messages that have differentpurposes are of different types. Depending on the type of the message,the system maintains different ordering and durability guarantees. Asthe persistent messages are being processed, the messages aretemporarily stored in multiple durable and non-durable storage hardwaretechnologies. In some embodiments, messages are stored in RAM, NVRAM andon NAND flash devices, and a variety of protocols are used in order tomake efficient use of each storage medium. Latency-sensitive clientrequests may be persisted in replicated NVRAM, and then later NAND,while background rebalancing operations are persisted directly to NAND.

Persistent messages are persistently stored prior to being transmitted.This allows the system to continue to serve client requests despitefailures and component replacement. Although many hardware componentscontain unique identifiers that are visible to system administrators,manufacturer, hardware supply chain and ongoing monitoring qualitycontrol infrastructure, applications running on top of theinfrastructure address virtualize addresses. These virtualized addressesdo not change over the lifetime of the storage system, regardless ofcomponent failures and replacements. This allows each component of thestorage system to be replaced over time without reconfiguration ordisruptions of client request processing, i.e. the system supportsnon-disruptive upgrades.

In some embodiments, the virtualized addresses are stored withsufficient redundancy. A continuous monitoring system correlateshardware and software status and the hardware identifiers. This allowsdetection and prediction of failures due to faulty components andmanufacturing details. The monitoring system also enables the proactivetransfer of authorities and entities away from impacted devices beforefailure occurs by removing the component from the critical path in someembodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode 150 and contents of a non-volatile solid state storage 152 of thestorage node 150. Data is communicated to and from the storage node 150by a network interface controller (‘NIC’) 202 in some embodiments. Eachstorage node 150 has a CPU 156, and one or more non-volatile solid statestorage 152, as discussed above. Moving down one level in FIG. 2C, eachnon-volatile solid state storage 152 has a relatively fast non-volatilesolid state memory, such as nonvolatile random access memory (‘NVRAM’)204, and flash memory 206. In some embodiments, NVRAM 204 may be acomponent that does not require program/erase cycles (DRAM, MRAM, PCM),and can be a memory that can support being written vastly more oftenthan the memory is read from. Moving down another level in FIG. 2C, theNVRAM 204 is implemented in one embodiment as high speed volatilememory, such as dynamic random access memory (DRAM) 216, backed up byenergy reserve 218. Energy reserve 218 provides sufficient electricalpower to keep the DRAM 216 powered long enough for contents to betransferred to the flash memory 206 in the event of power failure. Insome embodiments, energy reserve 218 is a capacitor, super-capacitor,battery, or other device, that supplies a suitable supply of energysufficient to enable the transfer of the contents of DRAM 216 to astable storage medium in the case of power loss. The flash memory 206 isimplemented as multiple flash dies 222, which may be referred to aspackages of flash dies 222 or an array of flash dies 222. It should beappreciated that the flash dies 222 could be packaged in any number ofways, with a single die per package, multiple dies per package (i.e.multichip packages), in hybrid packages, as bare dies on a printedcircuit board or other substrate, as encapsulated dies, etc. In theembodiment shown, the non-volatile solid state storage 152 has acontroller 212 or other processor, and an input output (I/O) port 210coupled to the controller 212. I/O port 210 is coupled to the CPU 156and/or the network interface controller 202 of the flash storage node150. Flash input output (I/O) port 220 is coupled to the flash dies 222,and a direct memory access unit (DMA) 214 is coupled to the controller212, the DRAM 216 and the flash dies 222. In the embodiment shown, theI/O port 210, controller 212, DMA unit 214 and flash I/O port 220 areimplemented on a programmable logic device (‘PLD’) 208, e.g., a fieldprogrammable gate array (FPGA). In this embodiment, each flash die 222has pages, organized as sixteen kB (kilobyte) pages 224, and a register226 through which data can be written to or read from the flash die 222.In further embodiments, other types of solid-state memory are used inplace of, or in addition to flash memory illustrated within flash die222.

Storage clusters 161, in various embodiments as disclosed herein, can becontrasted with storage arrays in general. The storage nodes 150 arepart of a collection that creates the storage cluster 161. Each storagenode 150 owns a slice of data and computing required to provide thedata. Multiple storage nodes 150 cooperate to store and retrieve thedata. Storage memory or storage devices, as used in storage arrays ingeneral, are less involved with processing and manipulating the data.Storage memory or storage devices in a storage array receive commands toread, write, or erase data. The storage memory or storage devices in astorage array are not aware of a larger system in which they areembedded, or what the data means. Storage memory or storage devices instorage arrays can include various types of storage memory, such as RAM,solid state drives, hard disk drives, etc. The storage units 152described herein have multiple interfaces active simultaneously andserving multiple purposes. In some embodiments, some of thefunctionality of a storage node 150 is shifted into a storage unit 152,transforming the storage unit 152 into a combination of storage unit 152and storage node 150. Placing computing (relative to storage data) intothe storage unit 152 places this computing closer to the data itself.The various system embodiments have a hierarchy of storage node layerswith different capabilities. By contrast, in a storage array, acontroller owns and knows everything about all of the data that thecontroller manages in a shelf or storage devices. In a storage cluster161, as described herein, multiple controllers in multiple storage units152 and/or storage nodes 150 cooperate in various ways (e.g., forerasure coding, data sharding, metadata communication and redundancy,storage capacity expansion or contraction, data recovery, and so on).

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes 150 and storage units 152 of FIGS. 2A-C. In thisversion, each storage unit 152 has a processor such as controller 212(see FIG. 2C), an FPGA (field programmable gate array), flash memory206, and NVRAM 204 (which is super-capacitor backed DRAM 216, see FIGS.2B and 2C) on a PCIe (peripheral component interconnect express) boardin a chassis 138 (see FIG. 2A). The storage unit 152 may be implementedas a single board containing storage, and may be the largest tolerablefailure domain inside the chassis. In some embodiments, up to twostorage units 152 may fail and the device will continue with no dataloss.

The physical storage is divided into named regions based on applicationusage in some embodiments. The NVRAM 204 is a contiguous block ofreserved memory in the storage unit 152 DRAM 216, and is backed by NANDflash. NVRAM 204 is logically divided into multiple memory regionswritten for two as spool (e.g., spool_region). Space within the NVRAM204 spools is managed by each authority 168 independently. Each deviceprovides an amount of storage space to each authority 168. Thatauthority 168 further manages lifetimes and allocations within thatspace. Examples of a spool include distributed transactions or notions.When the primary power to a storage unit 152 fails, onboardsuper-capacitors provide a short duration of power hold up. During thisholdup interval, the contents of the NVRAM 204 are flushed to flashmemory 206. On the next power-on, the contents of the NVRAM 204 arerecovered from the flash memory 206.

As for the storage unit controller, the responsibility of the logical“controller” is distributed across each of the blades containingauthorities 168. This distribution of logical control is shown in FIG.2D as a host controller 242, mid-tier controller 244 and storage unitcontroller(s) 246. Management of the control plane and the storage planeare treated independently, although parts may be physically co-locatedon the same blade. Each authority 168 effectively serves as anindependent controller. Each authority 168 provides its own data andmetadata structures, its own background workers, and maintains its ownlifecycle.

FIG. 2E is a blade 252 hardware block diagram, showing a control plane254, compute and storage planes 256, 258, and authorities 168interacting with underlying physical resources, using embodiments of thestorage nodes 150 and storage units 152 of FIGS. 2A-C in the storageserver environment of FIG. 2D. The control plane 254 is partitioned intoa number of authorities 168 which can use the compute resources in thecompute plane 256 to run on any of the blades 252. The storage plane 258is partitioned into a set of devices, each of which provides access toflash 206 and NVRAM 204 resources. In one embodiment, the compute plane256 may perform the operations of a storage array controller, asdescribed herein, on one or more devices of the storage plane 258 (e.g.,a storage array).

In the compute and storage planes 256, 258 of FIG. 2E, the authorities168 interact with the underlying physical resources (i.e. devices). Fromthe point of view of an authority 168, its resources are striped overall of the physical devices. From the point of view of a device, itprovides resources to all authorities 168, irrespective of where theauthorities happen to run. Each authority 168 has allocated or has beenallocated one or more partitions 260 of storage memory in the storageunits 152, e.g., partitions 260 in flash memory 206 and NVRAM 204. Eachauthority 168 uses those allocated partitions 260 that belong to it, forwriting or reading user data. Authorities can be associated withdiffering amounts of physical storage of the system. For example, oneauthority 168 could have a larger number of partitions 260 or largersized partitions 260 in one or more storage units 152 than one or moreother authorities 168.

FIG. 2F depicts elasticity software layers in blades 252 of a storagecluster, in accordance with some embodiments. In the elasticitystructure, elasticity software is symmetric, i.e. each blade's computemodule 270 runs the three identical layers of processes depicted in FIG.2F. Storage managers 274 execute read and write requests from otherblades 252 for data and metadata stored in local storage unit 152 NVRAM204 and flash 206. Authorities 168 fulfill client requests by issuingthe necessary reads and writes to the blades 252 on whose storage units152 the corresponding data or metadata resides. Endpoints 272 parseclient connection requests received from switch fabric 146 supervisorysoftware, relay the client connection requests to the authorities 168responsible for fulfillment, and relay the authorities' 168 responses toclients. The symmetric three-layer structure enables the storagesystem's high degree of concurrency. Elasticity scales out efficientlyand reliably in these embodiments. In addition, elasticity implements aunique scale-out technique that balances work evenly across allresources regardless of client access pattern, and maximizes concurrencyby eliminating much of the need for inter-blade coordination thattypically occurs with conventional distributed locking.

Still referring to FIG. 2F, authorities 168 running in the computemodules 270 of a blade 252 perform the internal operations required tofulfill client requests. One feature of elasticity is that authorities168 are stateless, i.e. they cache active data and metadata in their ownblades' 252 DRAMs for fast access, but the authorities store everyupdate in their NVRAM 204 partitions on three separate blades 252 untilthe update has been written to flash 206. All the storage system writesto NVRAM 204 are in triplicate to partitions on three separate blades252 in some embodiments. With triple-mirrored NVRAM 204 and persistentstorage protected by parity and Reed-Solomon RAID checksums, the storagesystem can survive concurrent failure of two blades 252 with no loss ofdata, metadata, or access to either.

Because authorities 168 are stateless, they can migrate between blades252. Each authority 168 has a unique identifier. NVRAM 204 and flash 206partitions are associated with authorities' 168 identifiers, not withthe blades 252 on which they are running in some. Thus, when anauthority 168 migrates, the authority 168 continues to manage the samestorage partitions from its new location. When a new blade 252 isinstalled in an embodiment of the storage cluster, the systemautomatically rebalances load by: partitioning the new blade's 252storage for use by the system's authorities 168, migrating selectedauthorities 168 to the new blade 252, starting endpoints 272 on the newblade 252 and including them in the switch fabric's 146 clientconnection distribution algorithm.

From their new locations, migrated authorities 168 persist the contentsof their NVRAM 204 partitions on flash 206, process read and writerequests from other authorities 168, and fulfill the client requeststhat endpoints 272 direct to them. Similarly, if a blade 252 fails or isremoved, the system redistributes its authorities 168 among the system'sremaining blades 252. The redistributed authorities 168 continue toperform their original functions from their new locations.

FIG. 2G depicts authorities 168 and storage resources in blades 252 of astorage cluster, in accordance with some embodiments. Each authority 168is exclusively responsible for a partition of the flash 206 and NVRAM204 on each blade 252. The authority 168 manages the content andintegrity of its partitions independently of other authorities 168.Authorities 168 compress incoming data and preserve it temporarily intheir NVRAM 204 partitions, and then consolidate, RAID-protect, andpersist the data in segments of the storage in their flash 206partitions. As the authorities 168 write data to flash 206, storagemanagers 274 perform the necessary flash translation to optimize writeperformance and maximize media longevity. In the background, authorities168 “garbage collect,” or reclaim space occupied by data that clientshave made obsolete by overwriting the data. It should be appreciatedthat since authorities' 168 partitions are disjoint, there is no needfor distributed locking to execute client and writes or to performbackground functions.

The embodiments described herein may utilize various software,communication and/or networking protocols. In addition, theconfiguration of the hardware and/or software may be adjusted toaccommodate various protocols. For example, the embodiments may utilizeActive Directory, which is a database based system that providesauthentication, directory, policy, and other services in a WINDOWS™environment. In these embodiments, LDAP (Lightweight Directory AccessProtocol) is one example application protocol for querying and modifyingitems in directory service providers such as Active Directory. In someembodiments, a network lock manager (‘NLM’) is utilized as a facilitythat works in cooperation with the Network File System (‘NFS’) toprovide a System V style of advisory file and record locking over anetwork. The Server Message Block (‘SMB’) protocol, one version of whichis also known as Common Internet File System (‘CIFS’), may be integratedwith the storage systems discussed herein. SMP operates as anapplication-layer network protocol typically used for providing sharedaccess to files, printers, and serial ports and miscellaneouscommunications between nodes on a network. SMB also provides anauthenticated inter-process communication mechanism. AMAZON™ S3 (SimpleStorage Service) is a web service offered by Amazon Web Services, andthe systems described herein may interface with Amazon S3 through webservices interfaces (REST (representational state transfer), SOAP(simple object access protocol), and BitTorrent). A RESTful API(application programming interface) breaks down a transaction to createa series of small modules. Each module addresses a particular underlyingpart of the transaction. The control or permissions provided with theseembodiments, especially for object data, may include utilization of anaccess control list (‘ACL’). The ACL is a list of permissions attachedto an object and the ACL specifies which users or system processes aregranted access to objects, as well as what operations are allowed ongiven objects. The systems may utilize Internet Protocol version 6(‘IPv6’), as well as IPv4, for the communications protocol that providesan identification and location system for computers on networks androutes traffic across the Internet. The routing of packets betweennetworked systems may include Equal-cost multi-path routing (‘ECMP’),which is a routing strategy where next-hop packet forwarding to a singledestination can occur over multiple “best paths” which tie for top placein routing metric calculations. Multi-path routing can be used inconjunction with most routing protocols, because it is a per-hopdecision limited to a single router. The software may supportMulti-tenancy, which is an architecture in which a single instance of asoftware application serves multiple customers. Each customer may bereferred to as a tenant. Tenants may be given the ability to customizesome parts of the application, but may not customize the application'scode, in some embodiments. The embodiments may maintain audit logs. Anaudit log is a document that records an event in a computing system. Inaddition to documenting what resources were accessed, audit log entriestypically include destination and source addresses, a timestamp, anduser login information for compliance with various regulations. Theembodiments may support various key management policies, such asencryption key rotation. In addition, the system may support dynamicroot passwords or some variation dynamically changing passwords.

FIG. 3A sets forth a diagram of a storage system 306 that is coupled fordata communications with a cloud services provider 302 in accordancewith some embodiments of the present disclosure. Although depicted inless detail, the storage system 306 depicted in FIG. 3A may be similarto the storage systems described above with reference to FIGS. 1A-1D andFIGS. 2A-2G. In some embodiments, the storage system 306 depicted inFIG. 3A may be embodied as a storage system that includes imbalancedactive/active controllers, as a storage system that includes balancedactive/active controllers, as a storage system that includesactive/active controllers where less than all of each controller'sresources are utilized such that each controller has reserve resourcesthat may be used to support failover, as a storage system that includesfully active/active controllers, as a storage system that includesdataset-segregated controllers, as a storage system that includesdual-layer architectures with front-end controllers and back-endintegrated storage controllers, as a storage system that includesscale-out clusters of dual-controller arrays, as well as combinations ofsuch embodiments.

In the example depicted in FIG. 3A, the storage system 306 is coupled tothe cloud services provider 302 via a data communications link 304. Thedata communications link 304 may be embodied as a dedicated datacommunications link, as a data communications pathway that is providedthrough the use of one or data communications networks such as a widearea network (‘WAN’) or local area network (‘LAN’), or as some othermechanism capable of transporting digital information between thestorage system 306 and the cloud services provider 302. Such a datacommunications link 304 may be fully wired, fully wireless, or someaggregation of wired and wireless data communications pathways. In suchan example, digital information may be exchanged between the storagesystem 306 and the cloud services provider 302 via the datacommunications link 304 using one or more data communications protocols.For example, digital information may be exchanged between the storagesystem 306 and the cloud services provider 302 via the datacommunications link 304 using the handheld device transfer protocol(‘HDTP’), hypertext transfer protocol (‘HTTP’), internet protocol(‘IP’), real-time transfer protocol (‘RTP’), transmission controlprotocol (‘TCP’), user datagram protocol (‘UDP’), wireless applicationprotocol (‘WAP’), or other protocol.

The cloud services provider 302 depicted in FIG. 3A may be embodied, forexample, as a system and computing environment that provides services tousers of the cloud services provider 302 through the sharing ofcomputing resources via the data communications link 304. The cloudservices provider 302 may provide on-demand access to a shared pool ofconfigurable computing resources such as computer networks, servers,storage, applications and services, and so on. The shared pool ofconfigurable resources may be rapidly provisioned and released to a userof the cloud services provider 302 with minimal management effort.Generally, the user of the cloud services provider 302 is unaware of theexact computing resources utilized by the cloud services provider 302 toprovide the services. Although in many cases such a cloud servicesprovider 302 may be accessible via the Internet, readers of skill in theart will recognize that any system that abstracts the use of sharedresources to provide services to a user through any data communicationslink may be considered a cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe configured to provide a variety of services to the storage system 306and users of the storage system 306 through the implementation ofvarious service models. For example, the cloud services provider 302 maybe configured to provide services to the storage system 306 and users ofthe storage system 306 through the implementation of an infrastructureas a service (‘IaaS’) service model where the cloud services provider302 offers computing infrastructure such as virtual machines and otherresources as a service to subscribers. In addition, the cloud servicesprovider 302 may be configured to provide services to the storage system306 and users of the storage system 306 through the implementation of aplatform as a service (‘PaaS’) service model where the cloud servicesprovider 302 offers a development environment to application developers.Such a development environment may include, for example, an operatingsystem, programming-language execution environment, database, webserver, or other components that may be utilized by applicationdevelopers to develop and run software solutions on a cloud platform.Furthermore, the cloud services provider 302 may be configured toprovide services to the storage system 306 and users of the storagesystem 306 through the implementation of a software as a service(‘SaaS’) service model where the cloud services provider 302 offersapplication software, databases, as well as the platforms that are usedto run the applications to the storage system 306 and users of thestorage system 306, providing the storage system 306 and users of thestorage system 306 with on-demand software and eliminating the need toinstall and run the application on local computers, which may simplifymaintenance and support of the application. The cloud services provider302 may be further configured to provide services to the storage system306 and users of the storage system 306 through the implementation of anauthentication as a service (‘AaaS’) service model where the cloudservices provider 302 offers authentication services that can be used tosecure access to applications, data sources, or other resources. Thecloud services provider 302 may also be configured to provide servicesto the storage system 306 and users of the storage system 306 throughthe implementation of a storage as a service model where the cloudservices provider 302 offers access to its storage infrastructure foruse by the storage system 306 and users of the storage system 306.Readers will appreciate that the cloud services provider 302 may beconfigured to provide additional services to the storage system 306 andusers of the storage system 306 through the implementation of additionalservice models, as the service models described above are included onlyfor explanatory purposes and in no way represent a limitation of theservices that may be offered by the cloud services provider 302 or alimitation as to the service models that may be implemented by the cloudservices provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe embodied, for example, as a private cloud, as a public cloud, or as acombination of a private cloud and public cloud. In an embodiment inwhich the cloud services provider 302 is embodied as a private cloud,the cloud services provider 302 may be dedicated to providing servicesto a single organization rather than providing services to multipleorganizations. In an embodiment where the cloud services provider 302 isembodied as a public cloud, the cloud services provider 302 may provideservices to multiple organizations. Public cloud and private clouddeployment models may differ and may come with various advantages anddisadvantages. For example, because a public cloud deployment involvesthe sharing of a computing infrastructure across different organization,such a deployment may not be ideal for organizations with securityconcerns, mission-critical workloads, uptime requirements demands, andso on. While a private cloud deployment can address some of theseissues, a private cloud deployment may require on-premises staff tomanage the private cloud. In still alternative embodiments, the cloudservices provider 302 may be embodied as a mix of a private and publiccloud services with a hybrid cloud deployment.

Although not explicitly depicted in FIG. 3A, readers will appreciatethat additional hardware components and additional software componentsmay be necessary to facilitate the delivery of cloud services to thestorage system 306 and users of the storage system 306. For example, thestorage system 306 may be coupled to (or even include) a cloud storagegateway. Such a cloud storage gateway may be embodied, for example, ashardware-based or software-based appliance that is located on premisewith the storage system 306. Such a cloud storage gateway may operate asa bridge between local applications that are executing on the storagearray 306 and remote, cloud-based storage that is utilized by thestorage array 306. Through the use of a cloud storage gateway,organizations may move primary iSCSI or NAS to the cloud servicesprovider 302, thereby enabling the organization to save space on theiron-premises storage systems. Such a cloud storage gateway may beconfigured to emulate a disk array, a block-based device, a file server,or other storage system that can translate the SCSI commands, fileserver commands, or other appropriate command into REST-space protocolsthat facilitate communications with the cloud services provider 302.

In order to enable the storage system 306 and users of the storagesystem 306 to make use of the services provided by the cloud servicesprovider 302, a cloud migration process may take place during whichdata, applications, or other elements from an organization's localsystems (or even from another cloud environment) are moved to the cloudservices provider 302. In order to successfully migrate data,applications, or other elements to the cloud services provider's 302environment, middleware such as a cloud migration tool may be utilizedto bridge gaps between the cloud services provider's 302 environment andan organization's environment. Such cloud migration tools may also beconfigured to address potentially high network costs and long transfertimes associated with migrating large volumes of data to the cloudservices provider 302, as well as addressing security concernsassociated with sensitive data to the cloud services provider 302 overdata communications networks. In order to further enable the storagesystem 306 and users of the storage system 306 to make use of theservices provided by the cloud services provider 302, a cloudorchestrator may also be used to arrange and coordinate automated tasksin pursuit of creating a consolidated process or workflow. Such a cloudorchestrator may perform tasks such as configuring various components,whether those components are cloud components or on-premises components,as well as managing the interconnections between such components. Thecloud orchestrator can simplify the inter-component communication andconnections to ensure that links are configured and maintained.

In the example depicted in FIG. 3A, and as described briefly above, thecloud services provider 302 may be configured to provide services to thestorage system 306 and users of the storage system 306 through the usageof a SaaS service model where the cloud services provider 302 offersapplication software, databases, as well as the platforms that are usedto run the applications to the storage system 306 and users of thestorage system 306, providing the storage system 306 and users of thestorage system 306 with on-demand software and eliminating the need toinstall and run the application on local computers, which may simplifymaintenance and support of the application. Such applications may takemany forms in accordance with various embodiments of the presentdisclosure. For example, the cloud services provider 302 may beconfigured to provide access to data analytics applications to thestorage system 306 and users of the storage system 306. Such dataanalytics applications may be configured, for example, to receivetelemetry data phoned home by the storage system 306. Such telemetrydata may describe various operating characteristics of the storagesystem 306 and may be analyzed, for example, to determine the health ofthe storage system 306, to identify workloads that are executing on thestorage system 306, to predict when the storage system 306 will run outof various resources, to recommend configuration changes, upgrades,workflow migrations, or other actions that improve the operation of thestorage system.

The cloud services provider 302 may also be configured to provide accessto virtualized computing environments to the storage system 306 andusers of the storage system 306. Such virtualized computing environmentsmay be embodied, for example, as a virtual machine or other virtualizedcomputer hardware platforms, virtual storage devices, virtualizedcomputer network resources, and so on. Examples of such virtualizedenvironments can include virtual machines that are created to emulate anactual computer, virtualized desktop environments that separate alogical desktop from a physical machine, virtualized file systems thatallow uniform access to different types of concrete file systems, andmany others.

For further explanation, FIG. 3B sets forth a diagram of a storagesystem 306 in accordance with some embodiments of the presentdisclosure. Although depicted in less detail, the storage system 306depicted in FIG. 3B may be similar to the storage systems describedabove with reference to FIGS. 1A-1D and FIGS. 2A-2G as the storagesystem may include many of the components described above.

The storage system 306 depicted in FIG. 3B may include storage resources308, which may be embodied in many forms. For example, in someembodiments the storage resources 308 can include nano-RAM or anotherform of nonvolatile random access memory that utilizes carbon nanotubesdeposited on a substrate. In some embodiments, the storage resources 308may include 3D crosspoint non-volatile memory in which bit storage isbased on a change of bulk resistance, in conjunction with a stackablecross-gridded data access array. In some embodiments, the storageresources 308 may include flash memory, including single-level cell(‘SLC’) NAND flash, multi-level cell (‘MLC’) NAND flash, triple-levelcell (‘TLC’) NAND flash, quad-level cell (‘QLC’) NAND flash, and others.In some embodiments, the storage resources 308 may include non-volatilemagnetoresistive random-access memory (‘MRAM’), including spin transfertorque (‘STT’) MRAM, in which data is stored through the use of magneticstorage elements. In some embodiments, the example storage resources 308may include non-volatile phase-change memory (‘PCM’) that may have theability to hold multiple bits in a single cell as cells can achieve anumber of distinct intermediary states. In some embodiments, the storageresources 308 may include quantum memory that allows for the storage andretrieval of photonic quantum information. In some embodiments, theexample storage resources 308 may include resistive random-access memory(‘ReRAM’) in which data is stored by changing the resistance across adielectric solid-state material. In some embodiments, the storageresources 308 may include storage class memory (‘SCM’) in whichsolid-state nonvolatile memory may be manufactured at a high densityusing some combination of sub-lithographic patterning techniques,multiple bits per cell, multiple layers of devices, and so on. Readerswill appreciate that other forms of computer memories and storagedevices may be utilized by the storage systems described above,including DRAM, SRAM, EEPROM, universal memory, and many others. Thestorage resources 308 depicted in FIG. 3A may be embodied in a varietyof form factors, including but not limited to, dual in-line memorymodules (‘DIMMs’), non-volatile dual in-line memory modules (‘NVDIMMs’),M.2, U.2, and others.

The storage resources 308 depicted in FIG. 3A may include various formsof storage-class memory (‘SCM’). SCM may effectively treat fast,non-volatile memory (e.g., NAND flash) as an extension of DRAM such thatan entire dataset may be treated as an in-memory dataset that residesentirely in DRAM. SCM may include non-volatile media such as, forexample, NAND flash. Such NAND flash may be accessed utilizing NVMe thatcan use the PCIe bus as its transport, providing for relatively lowaccess latencies compared to older protocols. In fact, the networkprotocols used for SSDs in all-flash arrays can include NVMe usingEthernet (ROCE, NVME TCP), Fibre Channel (NVMe FC), InfiniBand (iWARP),and others that make it possible to treat fast, non-volatile memory asan extension of DRAM. In view of the fact that DRAM is oftenbyte-addressable and fast, non-volatile memory such as NAND flash isblock-addressable, a controller software/hardware stack may be needed toconvert the block data to the bytes that are stored in the media.Examples of media and software that may be used as SCM can include, forexample, 3D XPoint, Intel Memory Drive Technology, Samsung's Z-SSD, andothers.

The example storage system 306 depicted in FIG. 3B may implement avariety of storage architectures. For example, storage systems inaccordance with some embodiments of the present disclosure may utilizeblock storage where data is stored in blocks, and each block essentiallyacts as an individual hard drive. Storage systems in accordance withsome embodiments of the present disclosure may utilize object storage,where data is managed as objects. Each object may include the dataitself, a variable amount of metadata, and a globally unique identifier,where object storage can be implemented at multiple levels (e.g., devicelevel, system level, interface level). Storage systems in accordancewith some embodiments of the present disclosure utilize file storage inwhich data is stored in a hierarchical structure. Such data may be savedin files and folders, and presented to both the system storing it andthe system retrieving it in the same format.

The example storage system 306 depicted in FIG. 3B may be embodied as astorage system in which additional storage resources can be addedthrough the use of a scale-up model, additional storage resources can beadded through the use of a scale-out model, or through some combinationthereof. In a scale-up model, additional storage may be added by addingadditional storage devices. In a scale-out model, however, additionalstorage nodes may be added to a cluster of storage nodes, where suchstorage nodes can include additional processing resources, additionalnetworking resources, and so on.

The storage system 306 depicted in FIG. 3B also includes communicationsresources 310 that may be useful in facilitating data communicationsbetween components within the storage system 306, as well as datacommunications between the storage system 306 and computing devices thatare outside of the storage system 306. The communications resources 310may be configured to utilize a variety of different protocols and datacommunication fabrics to facilitate data communications betweencomponents within the storage systems as well as computing devices thatare outside of the storage system. For example, the communicationsresources 310 can include fibre channel (‘FC’) technologies such as FCfabrics and FC protocols that can transport SCSI commands over FCnetworks. The communications resources 310 can also include FC overethernet (‘FCoE’) technologies through which FC frames are encapsulatedand transmitted over Ethernet networks. The communications resources 310can also include InfiniBand (‘IB’) technologies in which a switchedfabric topology is utilized to facilitate transmissions between channeladapters. The communications resources 310 can also include NVM Express(‘NVMe’) technologies and NVMe over fabrics (‘NVMeoF’) technologiesthrough which non-volatile storage media attached via a PCI express(‘PCIe’) bus may be accessed. The communications resources 310 can alsoinclude mechanisms for accessing storage resources 308 within thestorage system 306 utilizing serial attached SCSI (‘SAS’), serial ATA(‘SATA’) bus interfaces for connecting storage resources 308 within thestorage system 306 to host bus adapters within the storage system 306,internet small computer systems interface (‘iSCSI’) technologies toprovide block-level access to storage resources 308 within the storagesystem 306, and other communications resources that that may be usefulin facilitating data communications between components within thestorage system 306, as well as data communications between the storagesystem 306 and computing devices that are outside of the storage system306.

The storage system 306 depicted in FIG. 3B also includes processingresources 312 that may be useful in useful in executing computer programinstructions and performing other computational tasks within the storagesystem 306. The processing resources 312 may include one or moreapplication-specific integrated circuits (‘ASICs’) that are customizedfor some particular purpose as well as one or more central processingunits (‘CPUs’). The processing resources 312 may also include one ormore digital signal processors (‘DSPs’), one or more field-programmablegate arrays (‘FPGAs’), one or more systems on a chip (‘SoCs’), or otherform of processing resources 312. The storage system 306 may utilize thestorage resources 312 to perform a variety of tasks including, but notlimited to, supporting the execution of software resources 314 that willbe described in greater detail below.

The storage system 306 depicted in FIG. 3B also includes softwareresources 314 that, when executed by processing resources 312 within thestorage system 306, may perform various tasks. The software resources314 may include, for example, one or more modules of computer programinstructions that when executed by processing resources 312 within thestorage system 306 are useful in carrying out various data protectiontechniques to preserve the integrity of data that is stored within thestorage systems. Readers will appreciate that such data protectiontechniques may be carried out, for example, by system software executingon computer hardware within the storage system, by a cloud servicesprovider, or in other ways. Such data protection techniques can include,for example, data archiving techniques that cause data that is no longeractively used to be moved to a separate storage device or separatestorage system for long-term retention, data backup techniques throughwhich data stored in the storage system may be copied and stored in adistinct location to avoid data loss in the event of equipment failureor some other form of catastrophe with the storage system, datareplication techniques through which data stored in the storage systemis replicated to another storage system such that the data may beaccessible via multiple storage systems, data snapshotting techniquesthrough which the state of data within the storage system is captured atvarious points in time, data and database cloning techniques throughwhich duplicate copies of data and databases may be created, and otherdata protection techniques. Through the use of such data protectiontechniques, business continuity and disaster recovery objectives may bemet as a failure of the storage system may not result in the loss ofdata stored in the storage system.

The software resources 314 may also include software that is useful inimplementing software-defined storage (‘SDS’). In such an example, thesoftware resources 314 may include one or more modules of computerprogram instructions that, when executed, are useful in policy-basedprovisioning and management of data storage that is independent of theunderlying hardware. Such software resources 314 may be useful inimplementing storage virtualization to separate the storage hardwarefrom the software that manages the storage hardware.

The software resources 314 may also include software that is useful infacilitating and optimizing I/O operations that are directed to thestorage resources 308 in the storage system 306. For example, thesoftware resources 314 may include software modules that perform carryout various data reduction techniques such as, for example, datacompression, data deduplication, and others. The software resources 314may include software modules that intelligently group together I/Ooperations to facilitate better usage of the underlying storage resource308, software modules that perform data migration operations to migratefrom within a storage system, as well as software modules that performother functions. Such software resources 314 may be embodied as one ormore software containers or in many other ways.

Readers will appreciate that the presence of such software resources 314may provide for an improved user experience of the storage system 306,an expansion of functionality supported by the storage system 306, andmany other benefits. Consider the specific example of the softwareresources 314 carrying out data backup techniques through which datastored in the storage system may be copied and stored in a distinctlocation to avoid data loss in the event of equipment failure or someother form of catastrophe. In such an example, the systems describedherein may more reliably (and with less burden placed on the user)perform backup operations relative to interactive backup managementsystems that require high degrees of user interactivity, offer lessrobust automation and feature sets, and so on.

For further explanation, FIG. 3C sets forth an example of a cloud-basedstorage system 318 in accordance with some embodiments of the presentdisclosure. In the example depicted in FIG. 3C, the cloud-based storagesystem 318 is created entirely in a cloud computing environment 316 suchas, for example, Amazon Web Services (‘AWS’), Microsoft Azure, GoogleCloud Platform, IBM Cloud, Oracle Cloud, and others. The cloud-basedstorage system 318 may be used to provide services similar to theservices that may be provided by the storage systems described above.For example, the cloud-based storage system 318 may be used to provideblock storage services to users of the cloud-based storage system 318,the cloud-based storage system 318 may be used to provide storageservices to users of the cloud-based storage system 318 through the useof solid-state storage, and so on.

The cloud-based storage system 318 depicted in FIG. 3C includes twocloud computing instances 320, 322 that each are used to support theexecution of a storage controller application 324, 326. The cloudcomputing instances 320, 322 may be embodied, for example, as instancesof cloud computing resources (e.g., virtual machines) that may beprovided by the cloud computing environment 316 to support the executionof software applications such as the storage controller application 324,326. In one embodiment, the cloud computing instances 320, 322 may beembodied as Amazon Elastic Compute Cloud (‘EC2’) instances. In such anexample, an Amazon Machine Image (‘AMI’) that includes the storagecontroller application 324, 326 may be booted to create and configure avirtual machine that may execute the storage controller application 324,326.

In the example method depicted in FIG. 3C, the storage controllerapplication 324, 326 may be embodied as a module of computer programinstructions that, when executed, carries out various storage tasks. Forexample, the storage controller application 324, 326 may be embodied asa module of computer program instructions that, when executed, carriesout the same tasks as the controllers 110A, 110B in FIG. 1A describedabove such as writing data received from the users of the cloud-basedstorage system 318 to the cloud-based storage system 318, erasing datafrom the cloud-based storage system 318, retrieving data from thecloud-based storage system 318 and providing such data to users of thecloud-based storage system 318, monitoring and reporting of diskutilization and performance, performing redundancy operations, such asRAID or RAID-like data redundancy operations, compressing data,encrypting data, deduplicating data, and so forth. Readers willappreciate that because there are two cloud computing instances 320, 322that each include the storage controller application 324, 326, in someembodiments one cloud computing instance 320 may operate as the primarycontroller as described above while the other cloud computing instance322 may operate as the secondary controller as described above. In suchan example, in order to save costs, the cloud computing instance 320that operates as the primary controller may be deployed on a relativelyhigh-performance and relatively expensive cloud computing instance whilethe cloud computing instance 322 that operates as the secondarycontroller may be deployed on a relatively low-performance andrelatively inexpensive cloud computing instance. Readers will appreciatethat the storage controller application 324, 326 depicted in FIG. 3C mayinclude identical source code that is executed within different cloudcomputing instances 320, 322.

Consider an example in which the cloud computing environment 316 isembodied as AWS and the cloud computing instances are embodied as EC2instances. In such an example, AWS offers many types of EC2 instances.For example, AWS offers a suite of general purpose EC2 instances thatinclude varying levels of memory and processing power. In such anexample, the cloud computing instance 320 that operates as the primarycontroller may be deployed on one of the instance types that has arelatively large amount of memory and processing power while the cloudcomputing instance 322 that operates as the secondary controller may bedeployed on one of the instance types that has a relatively small amountof memory and processing power. In such an example, upon the occurrenceof a failover event where the roles of primary and secondary areswitched, a double failover may actually be carried out such that: 1) afirst failover event where the cloud computing instance 322 thatformerly operated as the secondary controller begins to operate as theprimary controller, and 2) a third cloud computing instance (not shown)that is of an instance type that has a relatively large amount of memoryand processing power is spun up with a copy of the storage controllerapplication, where the third cloud computing instance begins operatingas the primary controller while the cloud computing instance 322 thatoriginally operated as the secondary controller begins operating as thesecondary controller again. In such an example, the cloud computinginstance 320 that formerly operated as the primary controller may beterminated. Readers will appreciate that in alternative embodiments, thecloud computing instance 320 that is operating as the secondarycontroller after the failover event may continue to operate as thesecondary controller and the cloud computing instance 322 that operatedas the primary controller after the occurrence of the failover event maybe terminated once the primary role has been assumed by the third cloudcomputing instance.

Readers will appreciate that while the embodiments described aboverelate to embodiments where one cloud computing instance 320 operates asthe primary controller and the second cloud computing instance 322operates as the secondary controller, some embodiments are within thescope of the present disclosure. For example, each cloud computinginstance 320, 322 may operate as a primary controller for some portionof the address space supported by the cloud-based storage system 318,each cloud computing instance 320, 322 may operate as a primarycontroller where the servicing of I/O operations directed to thecloud-based storage system 318 are divided in some other way, and so on.In fact, in some embodiments where costs savings may be prioritized overperformance demands, only a single cloud computing instance may existthat contains the storage controller application. In such an example, acontroller failure may take more time to recover from as a new cloudcomputing instance that includes the storage controller applicationwould need to be spun up rather than having an already created cloudcomputing instance take on the role of servicing I/O operations thatwould have otherwise been handled by the failed cloud computinginstance.

The cloud-based storage system 318 depicted in FIG. 3C includes cloudcomputing instances 340 a, 340 b, 340 n with local storage 330, 334,338. The cloud computing instances 340 a, 340 b, 340 n depicted in FIG.3C may be embodied, for example, as instances of cloud computingresources that may be provided by the cloud computing environment 316 tosupport the execution of software applications. The cloud computinginstances 340 a, 340 b, 340 n of FIG. 3C may differ from the cloudcomputing instances 320, 322 described above as the cloud computinginstances 340 a, 340 b, 340 n of FIG. 3C have local storage 330, 334,338 resources whereas the cloud computing instances 320, 322 thatsupport the execution of the storage controller application 324, 326need not have local storage resources. The cloud computing instances 340a, 340 b, 340 n with local storage 330, 334, 338 may be embodied, forexample, as EC2 M5 instances that include one or more SSDs, as EC2 R5instances that include one or more SSDs, as EC2 I3 instances thatinclude one or more SSDs, and so on. In some embodiments, the localstorage 330, 334, 338 must be embodied as solid-state storage (e.g.,SSDs) rather than storage that makes use of hard disk drives.

In the example depicted in FIG. 3C, each of the cloud computinginstances 340 a, 340 b, 340 n with local storage 330, 334, 338 caninclude a software daemon 328, 332, 336 that, when executed by a cloudcomputing instance 340 a, 340 b, 340 n can present itself to the storagecontroller applications 324, 326 as if the cloud computing instance 340a, 340 b, 340 n were a physical storage device (e.g., one or more SSDs).In such an example, the software daemon 328, 332, 336 may includecomputer program instructions similar to those that would normally becontained on a storage device such that the storage controllerapplications 324, 326 can send and receive the same commands that astorage controller would send to storage devices. In such a way, thestorage controller applications 324, 326 may include code that isidentical to (or substantially identical to) the code that would beexecuted by the controllers in the storage systems described above. Inthese and similar embodiments, communications between the storagecontroller applications 324, 326 and the cloud computing instances 340a, 340 b, 340 n with local storage 330, 334, 338 may utilize iSCSI, NVMeover TCP, messaging, a custom protocol, or in some other mechanism.

In the example depicted in FIG. 3C, each of the cloud computinginstances 340 a, 340 b, 340 n with local storage 330, 334, 338 may alsobe coupled to block-storage 342, 344, 346 that is offered by the cloudcomputing environment 316. The block-storage 342, 344, 346 that isoffered by the cloud computing environment 316 may be embodied, forexample, as Amazon Elastic Block Store (‘EBS’) volumes. For example, afirst EBS volume may be coupled to a first cloud computing instance 340a, a second EBS volume may be coupled to a second cloud computinginstance 340 b, and a third EBS volume may be coupled to a third cloudcomputing instance 340 n. In such an example, the block-storage 342,344, 346 that is offered by the cloud computing environment 316 may beutilized in a manner that is similar to how the NVRAM devices describedabove are utilized, as the software daemon 328, 332, 336 (or some othermodule) that is executing within a particular cloud comping instance 340a, 340 b, 340 n may, upon receiving a request to write data, initiate awrite of the data to its attached EBS volume as well as a write of thedata to its local storage 330, 334, 338 resources. In some alternativeembodiments, data may only be written to the local storage 330, 334, 338resources within a particular cloud comping instance 340 a, 340 b, 340n. In an alternative embodiment, rather than using the block-storage342, 344, 346 that is offered by the cloud computing environment 316 asNVRAM, actual RAM on each of the cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338 may be used as NVRAM, therebydecreasing network utilization costs that would be associated with usingan EBS volume as the NVRAM.

In the example depicted in FIG. 3C, the cloud computing instances 340 a,340 b, 340 n with local storage 330, 334, 338 may be utilized, by cloudcomputing instances 320, 322 that support the execution of the storagecontroller application 324, 326 to service I/O operations that aredirected to the cloud-based storage system 318. Consider an example inwhich a first cloud computing instance 320 that is executing the storagecontroller application 324 is operating as the primary controller. Insuch an example, the first cloud computing instance 320 that isexecuting the storage controller application 324 may receive (directlyor indirectly via the secondary controller) requests to write data tothe cloud-based storage system 318 from users of the cloud-based storagesystem 318. In such an example, the first cloud computing instance 320that is executing the storage controller application 324 may performvarious tasks such as, for example, deduplicating the data contained inthe request, compressing the data contained in the request, determiningwhere to the write the data contained in the request, and so on, beforeultimately sending a request to write a deduplicated, encrypted, orotherwise possibly updated version of the data to one or more of thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338. Either cloud computing instance 320, 322, in some embodiments,may receive a request to read data from the cloud-based storage system318 and may ultimately send a request to read data to one or more of thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338.

Readers will appreciate that when a request to write data is received bya particular cloud computing instance 340 a, 340 b, 340 n with localstorage 330, 334, 338, the software daemon 328, 332, 336 or some othermodule of computer program instructions that is executing on theparticular cloud computing instance 340 a, 340 b, 340 n may beconfigured to not only write the data to its own local storage 330, 334,338 resources and any appropriate block-storage 342, 344, 346 that areoffered by the cloud computing environment 316, but the software daemon328, 332, 336 or some other module of computer program instructions thatis executing on the particular cloud computing instance 340 a, 340 b,340 n may also be configured to write the data to cloud-based objectstorage 348 that is attached to the particular cloud computing instance340 a, 340 b, 340 n. The cloud-based object storage 348 that is attachedto the particular cloud computing instance 340 a, 340 b, 340 n may beembodied, for example, as Amazon Simple Storage Service (‘S3’) storagethat is accessible by the particular cloud computing instance 340 a, 340b, 340 n. In some embodiments, the cloud computing instances 320, 322that each include the storage controller application 324, 326 mayinitiate the storage of the data in the local storage 330, 334, 338 ofthe cloud computing instances 340 a, 340 b, 340 n and the object storage348.

Readers will appreciate that, as described above, the cloud-basedstorage system 318 may be used to provide block storage services tousers of the cloud-based storage system 318. While the local storage330, 334, 338 resources and the block-storage 342, 344, 346 resourcesthat are utilized by the cloud computing instances 340 a, 340 b, 340 nmay support block-level access, the cloud-based object storage 348 thatis attached to the particular cloud computing instance 340 a, 340 b, 340n supports only object-based access. In order to address this, thesoftware daemon 328, 332, 336 or some other module of computer programinstructions that is executing on the particular cloud computinginstance 340 a, 340 b, 340 n may be configured to take blocks of data,package those blocks into objects, and write the objects to thecloud-based object storage 348 that is attached to the particular cloudcomputing instance 340 a, 340 b, 340 n.

Consider an example in which data is written to the local storage 330,334, 338 resources and the block-storage 342, 344, 346 resources thatare utilized by the cloud computing instances 340 a, 340 b, 340 n in 1MB blocks. In such an example, assume that a user of the cloud-basedstorage system 318 issues a request to write data that, after beingcompressed and deduplicated by the storage controller application 324,326 results in the need to write 5 MB of data. In such an example,writing the data to the local storage 330, 334, 338 resources and theblock-storage 342, 344, 346 resources that are utilized by the cloudcomputing instances 340 a, 340 b, 340 n is relatively straightforward as5 blocks that are 1 MB in size are written to the local storage 330,334, 338 resources and the block-storage 342, 344, 346 resources thatare utilized by the cloud computing instances 340 a, 340 b, 340 n. Insuch an example, the software daemon 328, 332, 336 or some other moduleof computer program instructions that is executing on the particularcloud computing instance 340 a, 340 b, 340 n may be configured to: 1)create a first object that includes the first 1 MB of data and write thefirst object to the cloud-based object storage 348, 2) create a secondobject that includes the second 1 MB of data and write the second objectto the cloud-based object storage 348, 3) create a third object thatincludes the third 1 MB of data and write the third object to thecloud-based object storage 348, and so on. As such, in some embodiments,each object that is written to the cloud-based object storage 348 may beidentical (or nearly identical) in size. Readers will appreciate that insuch an example, metadata that is associated with the data itself may beincluded in each object (e.g., the first 1 MB of the object is data andthe remaining portion is metadata associated with the data).

Readers will appreciate that the cloud-based object storage 348 may beincorporated into the cloud-based storage system 318 to increase thedurability of the cloud-based storage system 318. Continuing with theexample described above where the cloud computing instances 340 a, 340b, 340 n are EC2 instances, readers will understand that EC2 instancesare only guaranteed to have a monthly uptime of 99.9% and data stored inthe local instance store only persists during the lifetime of the EC2instance. As such, relying on the cloud computing instances 340 a, 340b, 340 n with local storage 330, 334, 338 as the only source ofpersistent data storage in the cloud-based storage system 318 may resultin a relatively unreliable storage system. Likewise, EBS volumes aredesigned for 99.999% availability. As such, even relying on EBS as thepersistent data store in the cloud-based storage system 318 may resultin a storage system that is not sufficiently durable. Amazon S3,however, is designed to provide 99.999999999% durability, meaning that acloud-based storage system 318 that can incorporate S3 into its pool ofstorage is substantially more durable than various other options.

Readers will appreciate that while a cloud-based storage system 318 thatcan incorporate S3 into its pool of storage is substantially moredurable than various other options, utilizing S3 as the primary pool ofstorage may result in storage system that has relatively slow responsetimes and relatively long I/O latencies. As such, the cloud-basedstorage system 318 depicted in FIG. 3C not only stores data in S3 butthe cloud-based storage system 318 also stores data in local storage330, 334, 338 resources and block-storage 342, 344, 346 resources thatare utilized by the cloud computing instances 340 a, 340 b, 340 n, suchthat read operations can be serviced from local storage 330, 334, 338resources and the block-storage 342, 344, 346 resources that areutilized by the cloud computing instances 340 a, 340 b, 340 n, therebyreducing read latency when users of the cloud-based storage system 318attempt to read data from the cloud-based storage system 318.

In some embodiments, all data that is stored by the cloud-based storagesystem 318 may be stored in both: 1) the cloud-based object storage 348,and 2) at least one of the local storage 330, 334, 338 resources orblock-storage 342, 344, 346 resources that are utilized by the cloudcomputing instances 340 a, 340 b, 340 n. In such embodiments, the localstorage 330, 334, 338 resources and block-storage 342, 344, 346resources that are utilized by the cloud computing instances 340 a, 340b, 340 n may effectively operate as cache that generally includes alldata that is also stored in S3, such that all reads of data may beserviced by the cloud computing instances 340 a, 340 b, 340 n withoutrequiring the cloud computing instances 340 a, 340 b, 340 n to accessthe cloud-based object storage 348. Readers will appreciate that in someembodiments, however, all data that is stored by the cloud-based storagesystem 318 may be stored in the cloud-based object storage 348, but lessthan all data that is stored by the cloud-based storage system 318 maybe stored in at least one of the local storage 330, 334, 338 resourcesor block-storage 342, 344, 346 resources that are utilized by the cloudcomputing instances 340 a, 340 b, 340 n. In such an example, variouspolicies may be utilized to determine which subset of the data that isstored by the cloud-based storage system 318 should reside in both: 1)the cloud-based object storage 348, and 2) at least one of the localstorage 330, 334, 338 resources or block-storage 342, 344, 346 resourcesthat are utilized by the cloud computing instances 340 a, 340 b, 340 n.

As described above, when the cloud computing instances 340 a, 340 b, 340n with local storage 330, 334, 338 are embodied as EC2 instances, thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338 are only guaranteed to have a monthly uptime of 99.9% and datastored in the local instance store only persists during the lifetime ofeach cloud computing instance 340 a, 340 b, 340 n with local storage330, 334, 338. As such, one or more modules of computer programinstructions that are executing within the cloud-based storage system318 (e.g., a monitoring module that is executing on its own EC2instance) may be designed to handle the failure of one or more of thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338. In such an example, the monitoring module may handle thefailure of one or more of the cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338 by creating one or more new cloudcomputing instances with local storage, retrieving data that was storedon the failed cloud computing instances 340 a, 340 b, 340 n from thecloud-based object storage 348, and storing the data retrieved from thecloud-based object storage 348 in local storage on the newly createdcloud computing instances. Readers will appreciate that many variants ofthis process may be implemented.

Consider an example in which all cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338 failed. In such an example, themonitoring module may create new cloud computing instances with localstorage, where high-bandwidth instances types are selected that allowfor the maximum data transfer rates between the newly createdhigh-bandwidth cloud computing instances with local storage and thecloud-based object storage 348. Readers will appreciate that instancestypes are selected that allow for the maximum data transfer ratesbetween the new cloud computing instances and the cloud-based objectstorage 348 such that the new high-bandwidth cloud computing instancescan be rehydrated with data from the cloud-based object storage 348 asquickly as possible. Once the new high-bandwidth cloud computinginstances are rehydrated with data from the cloud-based object storage348, less expensive lower-bandwidth cloud computing instances may becreated, data may be migrated to the less expensive lower-bandwidthcloud computing instances, and the high-bandwidth cloud computinginstances may be terminated.

Readers will appreciate that in some embodiments, the number of newcloud computing instances that are created may substantially exceed thenumber of cloud computing instances that are needed to locally store allof the data stored by the cloud-based storage system 318. The number ofnew cloud computing instances that are created may substantially exceedthe number of cloud computing instances that are needed to locally storeall of the data stored by the cloud-based storage system 318 in order tomore rapidly pull data from the cloud-based object storage 348 and intothe new cloud computing instances, as each new cloud computing instancecan (in parallel) retrieve some portion of the data stored by thecloud-based storage system 318. In such embodiments, once the datastored by the cloud-based storage system 318 has been pulled into thenewly created cloud computing instances, the data may be consolidatedwithin a subset of the newly created cloud computing instances and thosenewly created cloud computing instances that are excessive may beterminated.

Consider an example in which 1000 cloud computing instances are neededin order to locally store all valid data that users of the cloud-basedstorage system 318 have written to the cloud-based storage system 318.In such an example, assume that all 1,000 cloud computing instancesfail. In such an example, the monitoring module may cause 100,000 cloudcomputing instances to be created, where each cloud computing instanceis responsible for retrieving, from the cloud-based object storage 348,distinct 1/100,000th chunks of the valid data that users of thecloud-based storage system 318 have written to the cloud-based storagesystem 318 and locally storing the distinct chunk of the dataset that itretrieved. In such an example, because each of the 100,000 cloudcomputing instances can retrieve data from the cloud-based objectstorage 348 in parallel, the caching layer may be restored 100 timesfaster as compared to an embodiment where the monitoring module onlycreate 1000 replacement cloud computing instances. In such an example,over time the data that is stored locally in the 100,000 could beconsolidated into 1,000 cloud computing instances and the remaining99,000 instances could be terminated.

Readers will appreciate that various performance aspects of thecloud-based storage system 318 may be monitored (e.g., by a monitoringmodule that is executing in an EC2 instance) such that the cloud-basedstorage system 318 can be scaled-up or scaled-out as needed. Consider anexample in which the monitoring module monitors the performance of thecould-based storage system 318 via communications with one or more ofthe cloud computing instances 320, 322 that each are used to support theexecution of a storage controller application 324, 326, via monitoringcommunications between cloud computing instances 320, 322, 340 a, 340 b,340 n, via monitoring communications between cloud computing instances320, 322, 340 a, 340 b, 340 n and the cloud-based object storage 348, orin some other way. In such an example, assume that the monitoring moduledetermines that the cloud computing instances 320, 322 that are used tosupport the execution of a storage controller application 324, 326 areundersized and not sufficiently servicing the I/O requests that areissued by users of the cloud-based storage system 318. In such anexample, the monitoring module may create a new, more powerful cloudcomputing instance (e.g., a cloud computing instance of a type thatincludes more processing power, more memory, etc. . . . ) that includesthe storage controller application such that the new, more powerfulcloud computing instance can begin operating as the primary controller.Likewise, if the monitoring module determines that the cloud computinginstances 320, 322 that are used to support the execution of a storagecontroller application 324, 326 are oversized and that cost savingscould be gained by switching to a smaller, less powerful cloud computinginstance, the monitoring module may create a new, less powerful (andless expensive) cloud computing instance that includes the storagecontroller application such that the new, less powerful cloud computinginstance can begin operating as the primary controller.

Consider, as an additional example of dynamically sizing the cloud-basedstorage system 318, an example in which the monitoring module determinesthat the utilization of the local storage that is collectively providedby the cloud computing instances 340 a, 340 b, 340 n has reached apredetermined utilization threshold (e.g., 95%). In such an example, themonitoring module may create additional cloud computing instances withlocal storage to expand the pool of local storage that is offered by thecloud computing instances. Alternatively, the monitoring module maycreate one or more new cloud computing instances that have largeramounts of local storage than the already existing cloud computinginstances 340 a, 340 b, 340 n, such that data stored in an alreadyexisting cloud computing instance 340 a, 340 b, 340 n can be migrated tothe one or more new cloud computing instances and the already existingcloud computing instance 340 a, 340 b, 340 n can be terminated, therebyexpanding the pool of local storage that is offered by the cloudcomputing instances. Likewise, if the pool of local storage that isoffered by the cloud computing instances is unnecessarily large, datacan be consolidated and some cloud computing instances can beterminated.

Readers will appreciate that the cloud-based storage system 318 may besized up and down automatically by a monitoring module applying apredetermined set of rules that may be relatively simple of relativelycomplicated. In fact, the monitoring module may not only take intoaccount the current state of the cloud-based storage system 318, but themonitoring module may also apply predictive policies that are based on,for example, observed behavior (e.g., every night from 10 PM until 6 AMusage of the storage system is relatively light), predeterminedfingerprints (e.g., every time a virtual desktop infrastructure adds 100virtual desktops, the number of IOPS directed to the storage systemincrease by X), and so on. In such an example, the dynamic scaling ofthe cloud-based storage system 318 may be based on current performancemetrics, predicted workloads, and many other factors, includingcombinations thereof.

Readers will further appreciate that because the cloud-based storagesystem 318 may be dynamically scaled, the cloud-based storage system 318may even operate in a way that is more dynamic. Consider the example ofgarbage collection. In a traditional storage system, the amount ofstorage is fixed. As such, at some point the storage system may beforced to perform garbage collection as the amount of available storagehas become so constrained that the storage system is on the verge ofrunning out of storage. In contrast, the cloud-based storage system 318described here can always ‘add’ additional storage (e.g., by adding morecloud computing instances with local storage). Because the cloud-basedstorage system 318 described here can always ‘add’ additional storage,the cloud-based storage system 318 can make more intelligent decisionsregarding when to perform garbage collection. For example, thecloud-based storage system 318 may implement a policy that garbagecollection only be performed when the number of IOPS being serviced bythe cloud-based storage system 318 falls below a certain level. In someembodiments, other system-level functions (e.g., deduplication,compression) may also be turned off and on in response to system load,given that the size of the cloud-based storage system 318 is notconstrained in the same way that traditional storage systems areconstrained.

Readers will appreciate that embodiments of the present disclosureresolve an issue with block-storage services offered by some cloudcomputing environments as some cloud computing environments only allowfor one cloud computing instance to connect to a block-storage volume ata single time. For example, in Amazon AWS, only a single EC2 instancemay be connected to an EBS volume. Through the use of EC2 instances withlocal storage, embodiments of the present disclosure can offermulti-connect capabilities where multiple EC2 instances can connect toanother EC2 instance with local storage (‘a drive instance’). In suchembodiments, the drive instances may include software executing withinthe drive instance that allows the drive instance to support I/Odirected to a particular volume from each connected EC2 instance. Assuch, some embodiments of the present disclosure may be embodied asmulti-connect block storage services that may not include all of thecomponents depicted in FIG. 3C.

In some embodiments, especially in embodiments where the cloud-basedobject storage 348 resources are embodied as Amazon S3, the cloud-basedstorage system 318 may include one or more modules (e.g., a module ofcomputer program instructions executing on an EC2 instance) that areconfigured to ensure that when the local storage of a particular cloudcomputing instance is rehydrated with data from S3, the appropriate datais actually in S3. This issue arises largely because S3 implements aneventual consistency model where, when overwriting an existing object,reads of the object will eventually (but not necessarily immediately)become consistent and will eventually (but not necessarily immediately)return the overwritten version of the object. To address this issue, insome embodiments of the present disclosure, objects in S3 are neveroverwritten. Instead, a traditional ‘overwrite’ would result in thecreation of the new object (that includes the updated version of thedata) and the eventual deletion of the old object (that includes theprevious version of the data).

In some embodiments of the present disclosure, as part of an attempt tonever (or almost never) overwrite an object, when data is written to S3the resultant object may be tagged with a sequence number. In someembodiments, these sequence numbers may be persisted elsewhere (e.g., ina database) such that at any point in time, the sequence numberassociated with the most up-to-date version of some piece of data can beknown. In such a way, a determination can be made as to whether S3 hasthe most recent version of some piece of data by merely reading thesequence number associated with an object—and without actually readingthe data from S3. The ability to make this determination may beparticularly important when a cloud computing instance with localstorage crashes, as it would be undesirable to rehydrate the localstorage of a replacement cloud computing instance with out-of-date data.In fact, because the cloud-based storage system 318 does not need toaccess the data to verify its validity, the data can stay encrypted andaccess charges can be avoided.

For further explanation, FIG. 3D sets forth a block diagram illustratingan example cloud-based storage system (366) (occasionally referred tohereafter as a ‘virtual storage system’) in accordance with someembodiments of the present disclosure. Adapting a physical storagesystem implementation to run in typical cloud infrastructure, such as acloud computing environment (364) depicted herein, can be accomplishedby providing a place to run the software that implements the storagesystem on one or more storage system controllers, providing suitablereplacements for the hardware components that the storage systemsoftware would normally interact with, and adapting the highavailability (‘HA’) models used to keep the storage system running inthe case of failures or that keep it from losing data in the face oftransient failures that exceed what the storage system can tolerate andkeep running.

For context, this collection of descriptions relates to operation withinthe set of available advertised constructs typical to cloud-basedInfrastructure as a Service (‘IaaS’) platforms, roughly as that style ofcloud infrastructure is defined by Amazon Web Services, Microsoft Azure,Google Cloud Platform, and others. Typical available constructs, andtheir characteristics, within such cloud platforms include:

-   -   Compute instances, typically running as virtual machines        flexibly allocated to physical host servers.    -   A division of resources into separate geographic regions which        have faster and higher-bandwidth access to users, non-cloud        resources, and other cloud platform components within the same        geographic region.    -   A division of resources within geographic regions into zones        with separate survivability in cases of wide-scale data center        failures, network failures, power grid failures, administrative        mistakes, and so on. Resources within a particular cloud        platform that are in separate survivability zones within the        same geographic region generally have fairly high bandwidth and        reasonably low latency between each other.    -   Local instance storage, as hard drives or solid-state drives, or        as rack-local storage systems, providing private storage to a        compute instance. These are not necessarily intended for very        long-term use, can't be migrated as virtual machines migrate        between host systems, can't be shared between virtual machines,        and come with few durability guarantees due to their local        nature. These are typically reasonably inexpensive and are not        billed based on I/Os issued against them.    -   Relatively high-speed reasonably durable block stores which can        often be connected to only one virtual machine at a time, but        whose access can be migrated. An example of this is EBS in AWS.    -   Object stores, typically Amazon S3 or a protocol derived from        S3, which are very durable, surviving wide-spread outages        through inter-survivability zone and cross-geography        replication. Objects are easy to create (as a web service PUT        operation to create an object with a name within some bucket        associated with an account) and to retrieve, as a web service        GET operation), and parallel creates and retrievals across a        sufficient number of objects can yield enormous bandwidth. But        latency is very poor, and modifications or replacement of        objects complete in unpredictable amounts of time. Also,        availability, as opposed to durability, of object stores is        often not that great, though that is an issue with many services        running in cloud environments.    -   A variety of databases, including high-scale key-value store        databases with reasonable durability (similar to the high-speed        durable block stores) and convenient sets of atomic update        primitives. This disclosure frequently refers to one of these,        DynamoDB, which is a simple highly durable key/value store with        convenient atomic update primitives.

The elements of a virtual storage system (i.e. a cloud-based storagesystem (366)) from the context of this disclosure will each be expandedupon later. In many embodiments, the following minimal set of componentsand concepts for constructing and defining a virtual storage systembuilt on a cloud platform, as an analog to a more traditional physicalstorage system, can include:

-   -   Virtual storage system controllers, depicted in FIG. 3D as an        active storage system virtual controller compute instance (368)        and a standby active storage system virtual controller compute        instance (370). The virtual storage system controllers may be        running on compute instances somewhere within the cloud        platform's infrastructure. The virtual storage system        controllers could be running, for example, on virtual machines,        in containers, or on bare metal servers. The virtual storage        system controllers run the core storage system logic, taking in        I/O and configuration requests from client hosts (possibly        through intermediary servers, not shown here) or from        administrative interfaces or tools, and then configuring and        implementing the unique capabilities of the particular storage        system, including implementing, but not limited to, core file        system, block services, or object storage logic (for storage        systems that present file systems, block-based volumes, or        object stores to host clients), snapshots, replication,        migration services, provisioning, host connectivity management,        deduplication, compression, and so on. The virtual storage        system controllers are also referred to herein as        cloud-computing instances that are used to support the execution        of the storage controller application, storage system virtual        controller compute instances, local instance storage, virtual        controllers, and the like.    -   Virtual drives (372, 376, 380, 384), running on compute        instances, referred to herein as virtual storage device servers,        that present storage out of available components presented by        the cloud platform. A virtual drive represents something similar        to a storage device, such as a disk drive or solid-state drive,        within the context of a traditional physical storage system,        from the standpoint of the virtual storage system architecture        and implementation. The virtual drives are also referred to        herein as cloud computing instances with local storage, VDrives,        and the like.    -   A virtual storage system dataset. This is a defined collection        of data and metadata that represents coherently managed content        representing a collection of file systems, volumes, objects, and        so on.    -   Segments. These are medium sized chunks of data, for example in        the range of 1 megabyte to 64 megabytes, that hold some        combination of data and metadata that is written together as a        unit to storage. The virtual storage system may combine data and        metadata together into segments in a variety of ways, though        some descriptions reference parity segments, which are segments        that store calculated parity content based on an erasure code        (e.g., RAID-5 P and Q data) computed from the content of other        segments.

In the example depicted in FIG. 3D, other components include instancestores for volumes (374, 378, 382, 386), EBS NVRAM (388, 390), aDynamoDB member table (396), a DynamoDB segment seq # table (394), aswill be explained in greater detail below. The actual boundaries betweenvirtual storage system controllers and the virtual drive servers thathost virtual drives can be flexible. Implementations could merge thesetogether to support a scale-out storage system, for example. Since thevirtual drive servers are themselves support general purpose compute,the model lends itself to functions migrating between virtual storagesystem controllers and virtual drive servers, or lend themselves toother kinds of optimizations that are less common for traditionalphysical storage devices which may well include a general purpose CPUbut where that general purpose CPU is generally quite low powered andlacking in RAM for handling complex in-memory calculations.

The term virtual storage system logic may be used herein to generalizethe concept of the distributed programming that implements the corelogic of the storage system, however that logic is distributed betweenvirtual storage system controllers, scale-out implementations thatcombine virtual storage system controllers and virtual drive servers,and implementations that split or otherwise optimize processing betweenthe virtual storage system controllers and the virtual drive servers.

Implementing a highly available and reasonably high capacity storagesystem using a cloud platform should take into account the capabilitiesand limitations of readily available cloud platform components which arequite different from hardware components used in a physical storagesystem. There are block-based storage capabilities implemented withincloud platforms, such as Elastic Block Store, which could be used inplace of disk drives. These are expensive to use, though, are oftenbilled based on I/O traffic as well as capacity, and often don't supportconcurrent sharing between compute instances, all of which limits theirpracticality in creating highly available infrastructure that cansurvive compute instance failure by switching to another computeinstance or that can scale better by running storage system controllersoftware concurrently in several compute instances using the sameback-end block storage.

Local instance storage, such as the cloud computing instances with localstorage that are described above, is more practical from a coststandpoint but becomes unavailable if the physical host it is attachedto fails, and it can't be reattached to an alternate host if the failureis persistent. Local instance storage can be used to implement a virtualstorage device (e.g., the VDrives depicted in FIG. 3D) that can beshared to other compute instances that run storage system controllersoftware. That way, RAID-style software running in the storage systemcontroller compute instances can be used to tolerate failures of thecompute instances connected directly to instance storage that implementstorage devices.

For local instance storage to be cost effective on a per-byte basisvirtual storage “device server” compute instances might be attached to afew dozen to a few hundred terabytes, which is substantially larger thancurrent physical storage devices. This could mean that the minimum sizefor, say, a cost effective 6+2 RAID-6 storage system could be severalhundred terabytes to nearly a petabyte. To offset this, such a virtualdevice server might provide several “virtual drives” to several virtualstorage systems. Tolerating survivability zone failures can bechallenging, though, since geographic regions don't usually have enoughof them for effective erasure coding between them and crossinggeographic regions has substantial latency costs. Mirroring betweensurvivability zones can work at a capacity cost.

Object storage, such as the S3 bucket (392) depicted in FIG. 3D, is byfar the most durable form of at-scale storage available within currentcloud platforms. It has availability issues, but the probability of databeing lost or corrupted is exceedingly low. It has long (and somewhatunpredictable) latencies, though, making it poorly suited as the onlystorage for virtual storage systems that require something closer to theperformance characteristics of physical storage systems. With enoughparallel operations, data can be stored into and retrieved from objectstorage with very high bandwidth.

One way to make suitable storage devices with suitable performance,durability, and reliability for a virtual cloud-implemented storagesystem is to get performance from instance storage (e.g., theVDrives/cloud computing instances with local storage describedpreviously) and durability from object storage. In this model, a computeinstance with a local instance store implements a storage device, butcontent is also written to object stores. The local instance storeoperates either as a cache or as a complete copy of the data written toobject stores. Unfortunately, the high (and unpredictable) write latencyfor writing objects makes it impractical for the object store to containall recently written and acknowledged modifications to the storagesystem content. As a result, the very high durability of object storescan't quite be leveraged for zero RPO (recovery point objective)recoverable content if a goal is reasonably low-latency storage for thevirtual storage system, as will be addressed in greater detail below.

An interesting point of similarity between optimizing a storage systemfor flash storage and optimizing a storage system to write objects in anobject store is that both can be done by writing content inmoderate-sized chunks (on the order of a megabyte to a few megabytes)and leaving that content in place, unmodified, until some garbagecollection process frees up their content (through recognizing it is nolonger needed or from moving some remaining content elsewhere), at whichpoint it can be deleted, in the case of objects, or erased for futurereuse, in the case of flash storage. In the case of flash, theseorganize into erase blocks that must be “erased” before any reuse oroverwrite. In the case of objects, writing whole moderate-sized objectsat once and never modifying them is much more efficient and predictablethan writing smaller objects, writing them incrementally, or overwritingthem in place.

This creates an interesting point of architectural similarity that canbe leveraged by certain flash-optimized storage systems to adapt moreeasily to object storage. To handle this, a virtual drive implemented bya virtual storage device could implement a model whereby the storagesystem “writes” medium-sized chunks (on the order of, say, 1 to 32megabytes) which are received by a virtual device server and written toits local instance storage and into a unique object created for thatwrite. The object, or some combination of the object and object storebucket, could be named based on a storage system's dataset identifier,the virtual drive, and a logical offset, or based on the storagesystem's dataset identifier and some logical data segment identifierthat is separate from the virtual drive and a backward compatiblelogical offset.

An advantage of the first model for naming an object is that it canclosely mimic the layout characteristics of flash storage devicespresented to a storage system in a physical storage system. In thismodel, reading content from the object store backing a virtual deviceinvolves fetching as a data segment the object for the storage system'sdataset, for a particular virtual device, and at the particular logicaloffset the storage system associated with that segment. The equivalentof “erasing” a segment that has been garbage collected is to delete theobject named by the storage system dataset, the virtual drive, and thesegment offset. This leaves a problem if the storage system is going toreuse segments, since writing a new object with the same name as an oldobject runs into an issue with the “eventual consistency” model in someobject stores: specifically, when writing a new object with anever-before-used name, when the object's PUT completes, the object isguaranteed to be durable, but any other case (such as overwriting orreusing an object name) provides only a guarantee that once the changeis propagated to enough of the cloud platform's infrastructure it willbe durable, but there are no guarantees on when that will be. Reusingsegments is not a necessary feature if the storage system can bemodified to always generate new segment offsets, but that might requirechanging how the storage system understands its physically availablecapacity. Another issue is that deletions also have an eventualconsistency issue, so a later recovery of the dataset might encountersegments that the virtual storage system implementation had already“confirmed” were deleted which could cause confusion or corruption ifthe storage system isn't prepared for that.

One way to handle this issue with that first model for naming an objectis to map each fresh write of a segment at the same offset for a storagesystem dataset and virtual drive to a new name, such as by adding aunique “overwrite” or epoch identifier to the object name. An epochidentifier would change to a new value between the time one or moresegments are logically “erased” and the time any of those segments arelogically rewritten. An overwrite identifier would be associated witheach logical virtual drive segment offset and would advance for thatsegment somewhere between the time an erase is requested and the time itis rewritten. Then, to read an object for a segment, the virtual deviceserver would have to know the storage system dataset identifier, thevirtual drive identifier, the logical segment offset, and the epochidentifier or the overwrite identifier. The epoch or overwriteidentifier for a logical segment could be stored in a high-scalekey-value store database, such as DynamoDB, as long as it has similardurability to the object store.

If a virtual device server fails and a virtual storage drive's contenthas to be reconstituted on a new virtual device server by reading itsobjects from the backing object store, the new virtual device server canconnect to the associated segment overwrite table and use its content toconstruct how the objects for each segment offset are named. It can thenproceed to pull those objects in to be stored in the new virtual deviceserver's local instance storage, either on demand (if the local instancestorage is used as a cache) or as a high-bandwidth retrieval operationif the local instance storage is intended to be a complete copy of thevirtual drive. The overwrite identifier table can be used to determinewhich objects actual exist (and thus which logical offsets have storeddata), which are most current, and what their actual names are which canbe constructed from some combination of the virtual storage systemdataset identifier, the virtual drive, the logical offset, and theoverwrite or epoch identifier.

If a virtual storage server is expected to store the entire content of avirtual drive, and if a new virtual storage device server for a virtualdrive is expected to retrieve all that content before it can serve thevirtual drive, then a simple advancing epoch identifier associated witha virtual drive can eliminate the need for the key-value database.Instead, the new virtual storage device server can query the objectstore to retrieve all object names for a dataset and virtual devicecombination and can sort by logical offset and epoch identifier,ignoring content with the same logical offset. The virtual storagedevice server would then retrieve objects for each listed logical offsetbased on the logical offset's most recent epoch identifier (which can bereadily determined from the sort). When ready to serve the virtualdrive, the virtual storage device server will continue further advancingthe epoch identifier. This could require that when the epoch identifieris advanced that it is written into some database with durabilityroughly matching the object store, so that when a virtual storage deviceserver is rebooted, or a virtual drive is reconstituted on an alternatevirtual store device server, the epoch identifier can reliably advanceto a new value that has not previously been used. A higher-performancealternative persists epoch identifier into a database after some numberof advances and then advances the epoch identifier for the virtual driveby at least that same some number of advances when the virtual storagedevice server is rebooted or the virtual drive is reconstitutedelsewhere. This might be done, say, every 100 epoch advances, so that ona reboot or reconstitute, the current persisted value is retrieved andadvanced by at least that same 100.

An alternative to naming segments stored as objects by virtual driveoffset is to modify the storage system implementation to keep theconcept of stored moderate-sized segments that are garbage collected,but to change how they are indexed and stored. An important point isthat object stores are very durable and very resistant to corruption. Asa result, there is little reason to store erasure codes between objects.Instance stores, on the other hand, have a durability issue due to theirlocal nature. If a virtual storage device server or one of its localinstance store volumes were to fail, the content can be reconstructedfrom the backing object store, although this would involve a latencyhit. As a result, it may not make sense to continue using an erasurecoding scheme (e.g., some variant of RAID-6) across virtual drives usedby a storage system, but there are few reasons beyond implementationcompatibility to do the same for data stored in the object store.

As a result, it makes sense to store data into local instance storesacross virtual drives with erasure coded stripes of some kind, and tostore data in object stores with no erasure coding at all (at least noneunder the direction of the virtual storage system or the virtual storagedevice servers—the object store implementation may well make extensiveuse of erasure coding internally, but that is opaque to the virtualstorage system or the virtual storage device servers that we aredescribing here).

One simple way to accomplish virtual drives storing data but not parityinto object stores would be to keep the format the same, except thatvirtual drive content that was only erasure code check codes (e.g., Pand Q parity) wouldn't be stored into the object store. If such data hadto be reconstructed from objects in order to recreate the P and/or Qblocks for a segment on virtual drives, it could be reconstructed eitherfrom the local instance stores on other virtual drives that containmatching data blocks, or the data blocks would be retrieved from theobject store and the P and/or Q blocks would be calculated from them. Ifan entire virtual drive has to be reconstructed, such as to a replacedvirtual storage device server, the virtual storage system or the virtualstorage device server (whichever was managing the process or whicheverone was handling the network traffic and requests) would have a choiceof retrieving data blocks directly from the object store (which involvesretrieving and transferring over the network only the number of datablocks being recovered) or recalculating the content from the data and Por Q blocks from the local instance storage on other virtual storagedevices associated with each stripe (which involves retrieving andtransferring over the network N times as much data as is beingrecovered, where N is the number of data shards in the stripe). In an8+2 RAID-6-style stripe model, for example, if the parity shards areevenly scattered between virtual drives then 80% of the content of thevirtual drive could be rebuilt from the object store as retrievals andtransfers of that number of blocks, whereas 20% of the content (the Pand Q shards for the virtual drive) would be reconstructed bytransferring eight times as much data as the blocks being rebuilt eitherfrom local instance storage on other virtual storage device servers orfrom the object store or from some combination.

This implementation might require that the virtual storage system informthe virtual device server which blocks are data and which requirereconstruction from other shards. It might also require that data beindicated as check code data when it is being written (so that thevirtual device server knows it doesn't need to be mirrored to the objectstore), or it might require that the virtual storage systemimplementation inform the virtual storage system of the striping andsharding model for any data stripe the virtual storage system iswriting. Alternately, the striping scheme might be predictable orpre-configured for a virtual drive, or for addressable regions of avirtual drive, allowing the virtual storage device server to determineon its own whether a unit of blocks is data, which should be stored inan object store, or check code blocks, which do not need to be stored inan object store.

It should be noted that there is a disadvantage to avoiding the storingof check codes into the object store when it is still used for virtualdrives. If a complete virtual storage system dataset is to be recoveredinto virtual drives from an object store, then if the check code shardsare not stored in the object store, then all data retrieved from theobject store may need to be further transferred at least once betweenvirtual drives, since the check code shards (e.g., P and Q for aRAID-6-style erasure coded stripe) are calculated by combining the datashards of a stripe together using binary mathematical operators whichdepends on both operands (e.g., a block of data content and a partialresult) residing in memory on the same compute instance, with the resulteither transferred to (or computed within) the virtual storage deviceservers with the local instance storage that stores the check codeshards for an erasure coded stripe. If, instead, the check code shardsare also stored in the object store, then all content (including thosecheck codes) can be retrieved in parallel from the object store into allvirtual drives without any further network transfers between virtualstorage device servers. Whether one model or the other is preferable maydepend on the costs of network traffic between virtual storage deviceservers, the cost of retrieval from an object store, and the cost ofstoring otherwise unnecessary check data in the object store. The objectstore retrieval and storage costs can be reduced significantly by usingwider stripes. For example, with 20+2 stripes, the retrieval andcapacity overhead is only 10%. Then, full retrievals of an entirevirtual storage system dataset might be rare. The total network trafficfor rebuilding only one virtual drive, as long as check block shards areevenly scattered, ends up being about the same either way. For example,with a 10+2 stripe, 90% of the content of virtual drive can be retrieveddirectly from the object store, and 10% requires transferring 10 timesthat much data from other virtual storage device servers, which adds upto the same total network transfer. If, however, network transfersbetween virtual storage device server compute instances is significantlycheaper (e.g., less than 10% the cost) versus transfers from objectstorage, then it might be better to rebuild a single virtual driveentirely from other virtual drives, and the total network transfersavings from storing check blocks may be economically detrimental.

Another way to accomplish this goal (implementing the storage system byusing erasure codes for data stored in local instance stores acrossvirtual drives, while avoiding erasure codes for the same data stored inobject stores) is to separate the storing of segments across virtualdrives from the storing of segments into an object store. For example, aunit of data that the overall virtual storage system might call aprotected segment might by erasure coded across a set of virtual drives,for example as one 16 MB segment sharded and interleaved onto 8 virtualdrives of data and 2 virtual drives for parity protection, while thatsegment might be stored as a single 16 MB object in an object store.

This can create a bookkeeping problem, though, as the offset-basedindexing for each of the separate shards of the segment stored invirtual drives might not match the scheme that would be used for storingseparate objects. Objects would more naturally be stored using objectsnamed by a combination of a virtual storage system dataset identifierand a unique and non-reusable segment identifier. To handle this,references within indexes or other storage system metadata structuresmight need to include both offset-based addressing and segmentidentifier-based addressing.

An alternate way of handling the bookkeeping problem is to constructsegment identifier to virtual drive mappings as a separate structure. Aninteresting advantage to this is that the primary durable metadata forthe storage system would be based on what is store in the object store,and the virtual drive-oriented index could be reconstructed whenrebuilding a virtual storage system dataset from objects in an objectstore. This would then allow a dataset to be reconstructed into avirtual storage system incorporating a different number of virtual driveservers and virtual drives.

For further explanation, storage system segmenting models and how theymap to devices are discussed herein. In particular, there are at leasttwo different models to be discussed. In one model, the storage systemdefines a segment as a unit of data that is written out as a stripeacross multiple storage devices with a size on each device on the orderof a megabyte. The storage system's logical segment is thus N×B in size,where N is the number of devices that store data for the logical segmentand B is the size of the block stored on each device for the logicalsegment. If there are an additional R devices that store check codes,then the physical storage for the logical segment is (N+R)×B in size. Inthe other model, instead of data being striped at the block level,storage system segments are each written as a unit to a particularstorage device, and a set of storage system segments are organizedtogether for parity.

Such models open up a range of possibilities. The virtual drive couldmanage itself as a cache of a range of segments stored in the objectstore, with the segment identifier to virtual drive mapping held withinthe virtual drive itself, perhaps using local erasure coding to handlelocal drive failures (the implementation of local instance stores mayalready do this) and with fetches from the object store to recover fromuncorrected failures or to rebuild the range of segments onto a newvirtual drive, such as on a new virtual drive server, if a previousvirtual drive or virtual drive server fails.

Alternately a cache, or a complete copy of a virtual storage systemdataset, could be managed entirely through the virtual storage systemcontrollers, with virtual drives used to store segments as erasure codedstripes across virtual drives for recovery purposes, but without thevirtual storage system controllers necessarily knowing how to transfertheir content from object stores. As an optimization in this model, thevirtual storage system logic could inform virtual drive servers how totransfer segments to and from the object store while retaining controlover when and to which objects and segments within the virtual drivesthat happens. If segments are erasure coded across virtual drives, thenoffloading transfers to the virtual drive servers may save on networkand CPU bandwidth within the virtual storage system controllers, butdoesn't save on that much networking overall, since for an N+R erasurecode a virtual driver server that receives a segment will have totransfer N+R−1 shards of that segment to other virtual drive servers.

Alternately, segments could be stored on individual virtual drives (orcached on individual virtual drives), but a set of segments chosenacross multiple virtual drives could be linked together to form erasurecoded sets that store parity segments on an additional set of virtualdrives, so that many types of recoveries can operate within the set ofvirtual drives (between virtual drive servers, possibly by transferringdirectly or by transferring through virtual storage system controllers)but transfers of an individual segments to and from a virtual drivegenerally involves only the virtual drive and the other componentinvolved in the segment transfer. In this model, the virtual storagesystem logic, when storing a new segment, would store the segment intoone virtual drive and that segment would also be stored in an objectwithin the object store. A segment retrieved from the object store mightalso be stored as a segment within an individual virtual drive, eitheras a cache or as part of a complete transfer of a dataset into virtualdrives. Segments would then be protected by linking a set of thosesegments across a set of virtual drives and using their content tocalculate erasure code parity segments (e.g., P and Q parity segments ina RAID-6 style erasure code scheme). Later garbage collection operations(or perhaps cache discard operations) may have to account for the linkedset of segments, such as by discarding or garbage collecting theselinked segments (together with their parity segments) rather thangarbage collecting or discarding individual segments. In the case ofwriting new segments, it may make sense for virtual storage systemcontrollers to accumulate the contents of a set of segments in memoryand then calculate parity segments to be transferred to virtual drives,or the virtual storage system logic might inform the virtual drives whattransfers and calculations to perform in order to compute and storeparity segments. In the case of retrievals of segments from an objectstore, the virtual drives could manage this themselves, or the virtualstorage system logic could direct the virtual drives what transfers andcalculations to perform, or transfers could flow through virtual storagesystem controllers so they can calculate and store parity segments.

Virtual storage system logic could divide up segments between virtualdrives, or divide the work of transferring segments to and from theobject store, in a variety of ways.

Segment identifiers can be hashed to yield a virtual drive. Segmentidentifiers could include some kind of subset identifier that could bemapped to a virtual drive. Segments could be assigned dynamically to avirtual drive through a table. Within a virtual drive, a segment willlikely be stored at an offset within a local storage instance volume. Adynamic mapping of segments means that the durable format of a segmentlikely can't identify that offset, so there will likely need to be amapping table somewhere. That table could be managed by virtual driveservers, or it could be managed by virtual storage system controllers.If the virtual storage system controllers keep a table mapping segmentidentifiers to virtual drives, adding a virtual drive volume offset tothat table would be straightforward (though it would add to the memoryoverhead for that table within virtual storage system controller computeinstance memory).

The model of writing segments to individual drives, but linking segmentsacross virtual drives and writing parity segments to one or moreadditional virtual drives, presents another challenge: that ofdetermining which segments are linked together. This could be donethrough another table, or segment identifiers could include sequenceinformation that could rotate between virtual drives in a predictableway. Virtual storage system logic must account for this linkage whengarbage collecting, when discarding segments for other reasons, and whenrebuilding lost data. Of these, only rebuilding lost data has a narrowtimeliness element to it, as reading data in response to a client hostrequest is delayed by the time this takes, but even when one or twovirtual drives fail, most requests will be directed towards valid datasegments stored by non-failed drives, so introducing a table read forthat read (particularly compared to the overhead of reading from theother linked segments to rebuild missing data) isn't that big ofproblem. Virtual storage system logic could also use predictive readheuristics to lessen that latency hit.

Storage systems typically make use of some form of fast persistentmemory (or other low-latency, high-overwrite-rate storage) for stagingupdates. This allows quick acknowledgement of updates, and also allowstime for data to be organized for writing to longer-term persistentstorage. For example, with flash storage it allows time to fill up a setof segments to write into erase blocks and to then protect them witherasure codes across devices, or to add more localized protection aserasure codes or improved checksums within erase blocks or devices toprotect against page-level failures. In disk-based storage systems, thiscan provide time for read-calculate-and-write erasure code updates,while allowing larger work queues to build up for more efficientscheduling of transfers. This fast, persistent memory can also be usedto organize transactional updates to ensure consistency of relatedupdates written to backing stores (for example, writing a unit of dataand writing an index that references that unit of data, or handling amore complex set of manipulations that may be required to preserve thecontents of a snapshot while data for its related volume is beingoverwritten).

Fast persistent memory can be implemented in a physical storage systemas memory and a larger battery, as fast solid state drives, or even asPCI or NVMe or SCSI-connected connected devices with DRAM, a capacitoror other form of rechargeable power such as a rechargeable battery, andlocal flash, where the DRAM provides low latency and high overwriterates, and the capacitor or battery provides power to write the DRAM tothe flash on power failure. Moving forward, newer forms of non-volatilememory might be used such as 3D Xpoint or MRAM. To ensure that thisstaging memory can be used for recovering incomplete updates in the faceof storage system controller failures, this memory can either be dualported so that a second storage system controller can read its contentif a first storage system controller fails, or the content can be copiedby a first storage system controller over some interconnect to a secondstorage system controller and persisted on that controller as well as onthe first before any updates can be acknowledged. To handle failures ofthe persistent memory itself, there may be two or more such memories andany data is mirrored or erasure coded across them. These fast,persistent memories that are dual ported to multiple compute instancesdon't quite exist in many cloud platforms, so alternatives are required.Mirroring between virtual storage system controllers can work, thoughthere is still a lack of equivalently fast persistent memory.

With a model in place that allows a flexible relationship betweensegments stored in an object store and the choice of virtual drive andassociated local instance store to store a segment for lower latency andcheaper access, a virtual storage system can readily implement aflexible scaling model. Furthermore, the virtual storage systemcontrollers could divide up a namespace of segment identifiers in such away that segments are statistically evenly assigned to virtual drives,as part of dividing up work between virtual drives on available virtualdrive servers. This assignment could be based, for example, on a hashcalculated from the segment identifier, or the segment identifier itselfcould include some component that indicated some partitioning of theoverall dataset that can be assigned to a particular virtual drive. Ifthe number of these partitions, or the means of dividing up the segmentidentifiers into partitions through a hash resulted in a number ofpartitions that, was itself sufficiently larger than the current numberof virtual drives, then they could be assigned to the currently assignedor available number of virtual drives.

Instead of virtual drive servers handling this independently, virtualstorage system logic could either perform the operations to transferdata between objects in the object store and the virtual drives, orcould inform the virtual drive servers what transfers to perform. Intransfers were performed through the virtual storage system controllers,then the virtual drives could store erasure coded segments entirelyindependently of the segments stored into objects in the object store.If transfers are performed by the virtual drive servers underinstruction from the virtual storage system logic, then an alternatescheme may make sense. Then, if the number of virtual drives for adataset or a virtual storage system was increased or decreased in thefuture, such as in response to changes in an application or host clientor dataset or sub-dataset SLA or QOS policy or in response to changes inthe application, host client, dataset, or sub-dataset loadcharacteristics, or in response to performance interactions.

The storage systems described above may carry out intelligent databackup techniques through which data stored in the storage system may becopied and stored in a distinct location to avoid data loss in the eventof equipment failure or some other form of catastrophe. For example, thestorage systems described above may be configured to examine each backupto avoid restoring the storage system to an undesirable state. Consideran example in which malware infects the storage system. In such anexample, the storage system may include software resources 314 that canscan each backup to identify backups that were captured before themalware infected the storage system and those backups that were capturedafter the malware infected the storage system. In such an example, thestorage system may restore itself from a backup that does not includethe malware—or at least not restore the portions of a backup thatcontained the malware. In such an example, the storage system mayinclude software resources 314 that can scan each backup to identify thepresences of malware (or a virus, or some other undesirable), forexample, by identifying write operations that were serviced by thestorage system and originated from a network subnet that is suspected tohave delivered the malware, by identifying write operations that wereserviced by the storage system and originated from a user that issuspected to have delivered the malware, by identifying write operationsthat were serviced by the storage system and examining the content ofthe write operation against fingerprints of the malware, and in manyother ways.

Readers will further appreciate that the backups (often in the form ofone or more snapshots) may also be utilized to perform rapid recovery ofthe storage system. Consider an example in which the storage system isinfected with ransomware that locks users out of the storage system. Insuch an example, software resources 314 within the storage system may beconfigured to detect the presence of ransomware and may be furtherconfigured to restore the storage system to a point-in-time, using theretained backups, prior to the point-in-time at which the ransomwareinfected the storage system. In such an example, the presence ofransomware may be explicitly detected through the use of software toolsutilized by the system, through the use of a key (e.g., a USB drive)that is inserted into the storage system, or in a similar way. Likewise,the presence of ransomware may be inferred in response to systemactivity meeting a predetermined fingerprint such as, for example, noreads or writes coming into the system for a predetermined period oftime.

Readers will appreciate that the various components depicted in FIG. 3Bmay be grouped into one or more optimized computing packages asconverged infrastructures. Such converged infrastructures may includepools of computers, storage and networking resources that can be sharedby multiple applications and managed in a collective manner usingpolicy-driven processes. Such converged infrastructures may minimizecompatibility issues between various components within the storagesystem 306 while also reducing various costs associated with theestablishment and operation of the storage system 306. Such convergedinfrastructures may be implemented with a converged infrastructurereference architecture, with standalone appliances, with a softwaredriven hyper-converged approach (e.g., hyper-converged infrastructures),or in other ways.

Readers will appreciate that the storage system 306 depicted in FIG. 3Bmay be useful for supporting various types of software applications. Forexample, the storage system 306 may be useful in supporting artificialintelligence (‘AI’) applications, database applications, DevOpsprojects, electronic design automation tools, event-driven softwareapplications, high performance computing applications, simulationapplications, high-speed data capture and analysis applications, machinelearning applications, media production applications, media servingapplications, picture archiving and communication systems (‘PACS’)applications, software development applications, virtual realityapplications, augmented reality applications, and many other types ofapplications by providing storage resources to such applications.

The storage systems described above may operate to support a widevariety of applications. In view of the fact that the storage systemsinclude compute resources, storage resources, and a wide variety ofother resources, the storage systems may be well suited to supportapplications that are resource intensive such as, for example, AIapplications. Such AI applications may enable devices to perceive theirenvironment and take actions that maximize their chance of success atsome goal. Examples of such AI applications can include IBM Watson,Microsoft Oxford, Google DeepMind, Baidu Minwa, and others. The storagesystems described above may also be well suited to support other typesof applications that are resource intensive such as, for example,machine learning applications. Machine learning applications may performvarious types of data analysis to automate analytical model building.Using algorithms that iteratively learn from data, machine learningapplications can enable computers to learn without being explicitlyprogrammed. One particular area of machine learning is referred to asreinforcement learning, which involves taking suitable actions tomaximize reward in a particular situation. Reinforcement learning may beemployed to find the best possible behavior or path that a particularsoftware application or machine should take in a specific situation.Reinforcement learning differs from other areas of machine learning(e.g., supervised learning, unsupervised learning) in that correctinput/output pairs need not be presented for reinforcement learning andsub-optimal actions need not be explicitly corrected.

In addition to the resources already described, the storage systemsdescribed above may also include graphics processing units (‘GPUs’),occasionally referred to as visual processing unit (‘VPUs’). Such GPUsmay be embodied as specialized electronic circuits that rapidlymanipulate and alter memory to accelerate the creation of images in aframe buffer intended for output to a display device. Such GPUs may beincluded within any of the computing devices that are part of thestorage systems described above, including as one of many individuallyscalable components of a storage system, where other examples ofindividually scalable components of such storage system can includestorage components, memory components, compute components (e.g., CPUs,FPGAs, ASICs), networking components, software components, and others.In addition to GPUs, the storage systems described above may alsoinclude neural network processors (‘NNPs’) for use in various aspects ofneural network processing. Such NNPs may be used in place of (or inaddition to) GPUs and may also be independently scalable.

As described above, the storage systems described herein may beconfigured to support artificial intelligence applications, machinelearning applications, big data analytics applications, and many othertypes of applications. The rapid growth in these sort of applications isbeing driven by three technologies: deep learning (DL), GPU processors,and Big Data. Deep learning is a computing model that makes use ofmassively parallel neural networks inspired by the human brain. Insteadof experts handcrafting software, a deep learning model writes its ownsoftware by learning from lots of examples. A GPU is a modern processorwith thousands of cores, well-suited to run algorithms that looselyrepresent the parallel nature of the human brain.

Advances in deep neural networks have ignited a new wave of algorithmsand tools for data scientists to tap into their data with artificialintelligence (AI). With improved algorithms, larger data sets, andvarious frameworks (including open-source software libraries for machinelearning across a range of tasks), data scientists are tackling new usecases like autonomous driving vehicles, natural language processing andunderstanding, computer vision, machine reasoning, strong AI, and manyothers. Applications of such techniques may include: machine andvehicular object detection, identification and avoidance; visualrecognition, classification and tagging; algorithmic financial tradingstrategy performance management; simultaneous localization and mapping;predictive maintenance of high-value machinery; prevention against cybersecurity threats, expertise automation; image recognition andclassification; question answering; robotics; text analytics(extraction, classification) and text generation and translation; andmany others. Applications of AI techniques has materialized in a widearray of products include, for example, Amazon Echo's speech recognitiontechnology that allows users to talk to their machines, GoogleTranslate™ which allows for machine-based language translation,Spotify's Discover Weekly that provides recommendations on new songs andartists that a user may like based on the user's usage and trafficanalysis, Quill's text generation offering that takes structured dataand turns it into narrative stories, Chatbots that provide real-time,contextually specific answers to questions in a dialog format, and manyothers. Furthermore, AI may impact a wide variety of industries andsectors. For example, AI solutions may be used in healthcare to takeclinical notes, patient files, research data, and other inputs togenerate potential treatment options for doctors to explore. Likewise,AI solutions may be used by retailers to personalize consumerrecommendations based on a person's digital footprint of behaviors,profile data, or other data.

Training deep neural networks, however, requires both high quality inputdata and large amounts of computation. GPUs are massively parallelprocessors capable of operating on large amounts of data simultaneously.When combined into a multi-GPU cluster, a high throughput pipeline maybe required to feed input data from storage to the compute engines. Deeplearning is more than just constructing and training models. There alsoexists an entire data pipeline that must be designed for the scale,iteration, and experimentation necessary for a data science team tosucceed.

Data is the heart of modern AI and deep learning algorithms. Beforetraining can begin, one problem that must be addressed revolves aroundcollecting the labeled data that is crucial for training an accurate AImodel. A full scale AI deployment may be required to continuouslycollect, clean, transform, label, and store large amounts of data.Adding additional high quality data points directly translates to moreaccurate models and better insights. Data samples may undergo a seriesof processing steps including, but not limited to: 1) ingesting the datafrom an external source into the training system and storing the data inraw form, 2) cleaning and transforming the data in a format convenientfor training, including linking data samples to the appropriate label,3) exploring parameters and models, quickly testing with a smallerdataset, and iterating to converge on the most promising models to pushinto the production cluster, 4) executing training phases to selectrandom batches of input data, including both new and older samples, andfeeding those into production GPU servers for computation to updatemodel parameters, and 5) evaluating including using a holdback portionof the data not used in training in order to evaluate model accuracy onthe holdout data. This lifecycle may apply for any type of parallelizedmachine learning, not just neural networks or deep learning. Forexample, standard machine learning frameworks may rely on CPUs insteadof GPUs but the data ingest and training workflows may be the same.Readers will appreciate that a single shared storage data hub creates acoordination point throughout the lifecycle without the need for extradata copies among the ingest, preprocessing, and training stages. Rarelyis the ingested data used for only one purpose, and shared storage givesthe flexibility to train multiple different models or apply traditionalanalytics to the data.

Readers will appreciate that each stage in the AI data pipeline may havevarying requirements from the data hub (e.g., the storage system orcollection of storage systems). Scale-out storage systems must deliveruncompromising performance for all manner of access types andpatterns—from small, metadata-heavy to large files, from random tosequential access patterns, and from low to high concurrency. Thestorage systems described above may serve as an ideal AI data hub as thesystems may service unstructured workloads. In the first stage, data isideally ingested and stored on to the same data hub that followingstages will use, in order to avoid excess data copying. The next twosteps can be done on a standard compute server that optionally includesa GPU, and then in the fourth and last stage, full training productionjobs are run on powerful GPU-accelerated servers. Often, there is aproduction pipeline alongside an experimental pipeline operating on thesame dataset. Further, the GPU-accelerated servers can be usedindependently for different models or joined together to train on onelarger model, even spanning multiple systems for distributed training.If the shared storage tier is slow, then data must be copied to localstorage for each phase, resulting in wasted time staging data ontodifferent servers. The ideal data hub for the AI training pipelinedelivers performance similar to data stored locally on the server nodewhile also having the simplicity and performance to enable all pipelinestages to operate concurrently.

A data scientist works to improve the usefulness of the trained modelthrough a wide variety of approaches: more data, better data, smartertraining, and deeper models. In many cases, there will be teams of datascientists sharing the same datasets and working in parallel to producenew and improved training models. Often, there is a team of datascientists working within these phases concurrently on the same shareddatasets. Multiple, concurrent workloads of data processing,experimentation, and full-scale training layer the demands of multipleaccess patterns on the storage tier. In other words, storage cannot justsatisfy large file reads, but must contend with a mix of large and smallfile reads and writes. Finally, with multiple data scientists exploringdatasets and models, it may be critical to store data in its nativeformat to provide flexibility for each user to transform, clean, and usethe data in a unique way. The storage systems described above mayprovide a natural shared storage home for the dataset, with dataprotection redundancy (e.g., by using RAID6) and the performancenecessary to be a common access point for multiple developers andmultiple experiments. Using the storage systems described above mayavoid the need to carefully copy subsets of the data for local work,saving both engineering and GPU-accelerated servers use time. Thesecopies become a constant and growing tax as the raw data set and desiredtransformations constantly update and change.

Readers will appreciate that a fundamental reason why deep learning hasseen a surge in success is the continued improvement of models withlarger data set sizes. In contrast, classical machine learningalgorithms, like logistic regression, stop improving in accuracy atsmaller data set sizes. As such, the separation of compute resources andstorage resources may also allow independent scaling of each tier,avoiding many of the complexities inherent in managing both together. Asthe data set size grows or new data sets are considered, a scale outstorage system must be able to expand easily. Similarly, if moreconcurrent training is required, additional GPUs or other computeresources can be added without concern for their internal storage.Furthermore, the storage systems described above may make building,operating, and growing an AI system easier due to the random readbandwidth provided by the storage systems, the ability to of the storagesystems to randomly read small files (50 KB) high rates (meaning that noextra effort is required to aggregate individual data points to makelarger, storage-friendly files), the ability of the storage systems toscale capacity and performance as either the dataset grows or thethroughput requirements grow, the ability of the storage systems tosupport files or objects, the ability of the storage systems to tuneperformance for large or small files (i.e. no need for the user toprovision filesystems), the ability of the storage systems to supportnon-disruptive upgrades of hardware and software even during productionmodel training, and for many other reasons.

Small file performance of the storage tier may be critical as many typesof inputs, including text, audio, or images will be natively stored assmall files. If the storage tier does not handle small files well, anextra step will be required to pre-process and group samples into largerfiles. Storage, built on top of spinning disks, that relies on SSD as acaching tier, may fall short of the performance needed. Because trainingwith random input batches results in more accurate models, the entiredata set must be accessible with full performance. SSD caches onlyprovide high performance for a small subset of the data and will beineffective at hiding the latency of spinning drives.

Although the preceding paragraphs discuss deep learning applications,readers will appreciate that the storage systems described herein mayalso be part of a distributed deep learning (‘DDL’) platform to supportthe execution of DDL algorithms. Distributed deep learning may can beused to significantly accelerate deep learning with distributedcomputing on GPUs (or other form of accelerator or computer programinstruction executor), such that parallelism can be achieved. Inaddition, the output of training machine learning and deep learningmodels, such as a fully trained machine learning model, may be used fora variety of purposes and in conjunction with other tools. For example,trained machine learning models may be used in conjunction with toolslike Core ML to integrate a broad variety of machine learning modeltypes into an application. In fact, trained models may be run throughCore ML converter tools and inserted into a custom application that canbe deployed on compatible devices. The storage systems described abovemay also be paired with other technologies such as TensorFlow, anopen-source software library for dataflow programming across a range oftasks that may be used for machine learning applications such as neuralnetworks, to facilitate the development of such machine learning models,applications, and so on.

Readers will further appreciate that the systems described above may bedeployed in a variety of ways to support the democratization of AI, asAI becomes more available for mass consumption. The democratization ofAI may include, for example, the ability to offer AI as aPlatform-as-a-Service, the growth of Artificial general intelligenceofferings, the proliferation of Autonomous level 4 and Autonomous level5 vehicles, the availability of autonomous mobile robots, thedevelopment of conversational AI platforms, and many others. Forexample, the systems described above may be deployed in cloudenvironments, edge environments, or other environments that are usefulin supporting the democratization of AI. As part of the democratizationof AI, a movement may occur from narrow AI that consists of highlyscoped machine learning solutions that target a particular task toartificial general intelligence where the use of machine learning isexpanded to handle a broad range of use cases that could essentiallyperform any intelligent task that a human could perform and could learndynamically, much like a human.

The storage systems described above may also be used in a neuromorphiccomputing environment. Neuromorphic computing is a form of computingthat mimics brain cells. To support neuromorphic computing, anarchitecture of interconnected “neurons” replace traditional computingmodels with low-powered signals that go directly between neurons formore efficient computation. Neuromorphic computing may make use ofvery-large-scale integration (VLSI) systems containing electronic analogcircuits to mimic neuro-biological architectures present in the nervoussystem, as well as analog, digital, mixed-mode analog/digital VLSI, andsoftware systems that implement models of neural systems for perception,motor control, or multisensory integration.

Readers will appreciate that the storage systems described above may beconfigured to support the storage or use of (among other types of data)blockchains. Such blockchains may be embodied as a continuously growinglist of records, called blocks, which are linked and secured usingcryptography. Each block in a blockchain may contain a hash pointer as alink to a previous block, a timestamp, transaction data, and so on.Blockchains may be designed to be resistant to modification of the dataand can serve as an open, distributed ledger that can recordtransactions between two parties efficiently and in a verifiable andpermanent way. This makes blockchains potentially suitable for therecording of events, medical records, and other records managementactivities, such as identity management, transaction processing, andothers. In addition to supporting the storage and use of blockchaintechnologies, the storage systems described above may also support thestorage and use of derivative items such as, for example, open sourceblockchains and related tools that are part of the IBM′ Hyperledgerproject, permissioned blockchains in which a certain number of trustedparties are allowed to access the block chain, blockchain products thatenable developers to build their own distributed ledger projects, andothers. Readers will appreciate that blockchain technologies may impacta wide variety of industries and sectors. For example, blockchaintechnologies may be used in real estate transactions as blockchain basedcontracts whose use can eliminate the need for 3^(rd) parties and enableself-executing actions when conditions are met. Likewise, universalhealth records can be created by aggregating and placing a person'shealth history onto a blockchain ledger for any healthcare provider, orpermissioned health care providers, to access and update.

Readers will appreciate that the usage of blockchains is not limited tofinancial transactions, contracts, and the like. In fact, blockchainsmay be leveraged to enable the decentralized aggregation, ordering,timestamping and archiving of any type of information, includingstructured data, correspondence, documentation, or other data. Throughthe usage of blockchains, participants can provably and permanentlyagree on exactly what data was entered, when and by whom, withoutrelying on a trusted intermediary. For example, SAP's recently launchedblockchain platform, which supports MultiChain and Hyperledger Fabric,targets a broad range of supply chain and other non-financialapplications.

One way to use a blockchain for recording data is to embed each piece ofdata directly inside a transaction. Every blockchain transaction may bedigitally signed by one or more parties, replicated to a plurality ofnodes, ordered and timestamped by the chain's consensus algorithm, andstored permanently in a tamper-proof way. Any data within thetransaction will therefore be stored identically but independently byevery node, along with a proof of who wrote it and when. The chain'susers are able to retrieve this information at any future time. Thistype of storage may be referred to as on-chain storage. On-chain storagemay not be particularly practical, however, when attempting to store avery large dataset. As such, in accordance with embodiments of thepresent disclosure, blockchains and the storage systems described hereinmay be leveraged to support on-chain storage of data as well asoff-chain storage of data.

Off-chain storage of data can be implemented in a variety of ways andcan occur when the data itself is not stored within the blockchain. Forexample, in one embodiment, a hash function may be utilized and the dataitself may be fed into the hash function to generate a hash value. Insuch an example, the hashes of large pieces of data may be embeddedwithin transactions, instead of the data itself. Each hash may serve asa commitment to its input data, with the data itself being storedoutside of the blockchain. Readers will appreciate that any blockchainparticipant that needs an off-chain piece of data cannot reproduce thedata from its hash, but if the data can be retrieved in some other way,then the on-chain hash serves to confirm who created it and when. Justlike regular on-chain data, the hash may be embedded inside a digitallysigned transaction, which was included in the chain by consensus.

Readers will appreciate that, in some embodiments, alternatives toblockchains may be used to facilitate the decentralized storage ofinformation. For example, one alternative to a blockchain that may beused is a blockweave. While conventional blockchains store everytransaction to achieve validation, a blockweave permits securedecentralization without the usage of the entire chain, thereby enablinglow cost on-chain storage of data. Such blockweaves may utilize aconsensus mechanism that is based on proof of access (PoA) and proof ofwork (PoW). While typical PoW systems only depend on the previous blockin order to generate each successive block, the PoA algorithm mayincorporate data from a randomly chosen previous block. Combined withthe blockweave data structure, miners do not need to store all blocks(forming a blockchain), but rather can store any previous blocks forminga weave of blocks (a blockweave). This enables increased levels ofscalability, speed and low-cost and reduces the cost of data storage inpart because miners need not store all blocks, thereby resulting in asubstantial reduction in the amount of electricity that is consumedduring the mining process because, as the network expands, electricityconsumption decreases because a blockweave demands less and less hashingpower for consensus as data is added to the system. Furthermore,blockweaves may be deployed on a decentralized storage network in whichincentives are created to encourage rapid data sharing. Suchdecentralized storage networks may also make use of blockshadowingtechniques, where nodes only send a minimal block “shadow” to othernodes that allows peers to reconstruct a full block, instead oftransmitting the full block itself.

The storage systems described above may, either alone or in combinationwith other computing devices, be used to support in-memory computingapplications. In memory computing involves the storage of information inRAM that is distributed across a cluster of computers. In-memorycomputing helps business customers, including retailers, banks andutilities, to quickly detect patterns, analyze massive data volumes onthe fly, and perform their operations quickly. Readers will appreciatethat the storage systems described above, especially those that areconfigurable with customizable amounts of processing resources, storageresources, and memory resources (e.g., those systems in which bladesthat contain configurable amounts of each type of resource), may beconfigured in a way so as to provide an infrastructure that can supportin-memory computing. Likewise, the storage systems described above mayinclude component parts (e.g., NVDIMMs, 3D crosspoint storage thatprovide fast random access memory that is persistent) that can actuallyprovide for an improved in-memory computing environment as compared toin-memory computing environments that rely on RAM distributed acrossdedicated servers.

In some embodiments, the storage systems described above may beconfigured to operate as a hybrid in-memory computing environment thatincludes a universal interface to all storage media (e.g., RAM, flashstorage, 3D crosspoint storage). In such embodiments, users may have noknowledge regarding the details of where their data is stored but theycan still use the same full, unified API to address data. In suchembodiments, the storage system may (in the background) move data to thefastest layer available—including intelligently placing the data independence upon various characteristics of the data or in dependenceupon some other heuristic. In such an example, the storage systems mayeven make use of existing products such as Apache Ignite and GridGain tomove data between the various storage layers, or the storage systems maymake use of custom software to move data between the various storagelayers. The storage systems described herein may implement variousoptimizations to improve the performance of in-memory computing such as,for example, having computations occur close to the data.

Readers will further appreciate that in some embodiments, the storagesystems described above may be paired with other resources to supportthe applications described above. For example, one infrastructure couldinclude primary compute in the form of servers and workstations whichspecialize in using General-purpose computing on graphics processingunits (‘GPGPU’) to accelerate deep learning applications that areinterconnected into a computation engine to train parameters for deepneural networks. Each system may have Ethernet external connectivity,InfiniBand external connectivity, some other form of externalconnectivity, or some combination thereof. In such an example, the GPUscan be grouped for a single large training or used independently totrain multiple models. The infrastructure could also include a storagesystem such as those described above to provide, for example, ascale-out all-flash file or object store through which data can beaccessed via high-performance protocols such as NFS, S3, and so on. Theinfrastructure can also include, for example, redundant top-of-rackEthernet switches connected to storage and compute via ports in MLAGport channels for redundancy. The infrastructure could also includeadditional compute in the form of whitebox servers, optionally withGPUs, for data ingestion, pre-processing, and model debugging. Readerswill appreciate that additional infrastructures are also be possible.

Readers will appreciate that the systems described above may be bettersuited for the applications described above relative to other systemsthat may include, for example, a distributed direct-attached storage(DDAS) solution deployed in server nodes. Such DDAS solutions may bebuilt for handling large, less sequential accesses but may be less ableto handle small, random accesses. Readers will further appreciate thatthe storage systems described above may be utilized to provide aplatform for the applications described above that is preferable to theutilization of cloud-based resources as the storage systems may beincluded in an on-site or in-house infrastructure that is more secure,more locally and internally managed, more robust in feature sets andperformance, or otherwise preferable to the utilization of cloud-basedresources as part of a platform to support the applications describedabove. For example, services built on platforms such as IBM's Watson mayrequire a business enterprise to distribute individual user information,such as financial transaction information or identifiable patientrecords, to other institutions. As such, cloud-based offerings of AI asa service may be less desirable than internally managed and offered AIas a service that is supported by storage systems such as the storagesystems described above, for a wide array of technical reasons as wellas for various business reasons.

Readers will appreciate that the storage systems described above, eitheralone or in coordination with other computing machinery may beconfigured to support other AI related tools. For example, the storagesystems may make use of tools like ONXX or other open neural networkexchange formats that make it easier to transfer models written indifferent AI frameworks. Likewise, the storage systems may be configuredto support tools like Amazon's Gluon that allow developers to prototype,build, and train deep learning models. In fact, the storage systemsdescribed above may be part of a larger platform, such as IBM′ CloudPrivate for Data, that includes integrated data science, dataengineering and application building services. Such platforms mayseamlessly collect, organize, secure, and analyze data across anenterprise, as well as simplify hybrid data management, unified datagovernance and integration, data science and business analytics with asingle solution.

Readers will further appreciate that the storage systems described abovemay also be deployed as an edge solution. Such an edge solution may bein place to optimize cloud computing systems by performing dataprocessing at the edge of the network, near the source of the data. Edgecomputing can push applications, data and computing power (i.e.services) away from centralized points to the logical extremes of anetwork. Through the use of edge solutions such as the storage systemsdescribed above, computational tasks may be performed using the computeresources provided by such storage systems, data may be storage usingthe storage resources of the storage system, and cloud-based servicesmay be accessed through the use of various resources of the storagesystem (including networking resources). By performing computationaltasks on the edge solution, storing data on the edge solution, andgenerally making use of the edge solution, the consumption of expensivecloud-based resources may be avoided and, in fact, performanceimprovements may be experienced relative to a heavier reliance oncloud-based resources.

While many tasks may benefit from the utilization of an edge solution,some particular uses may be especially suited for deployment in such anenvironment. For example, devices like drones, autonomous cars, robots,and others may require extremely rapid processing—so fast, in fact, thatsending data up to a cloud environment and back to receive dataprocessing support may simply be too slow. Likewise, machines likelocomotives and gas turbines that generate large amounts of informationthrough the use of a wide array of data-generating sensors may benefitfrom the rapid data processing capabilities of an edge solution. As anadditional example, some IoT devices such as connected video cameras maynot be well-suited for the utilization of cloud-based resources as itmay be impractical (not only from a privacy perspective, securityperspective, or a financial perspective) to send the data to the cloudsimply because of the pure volume of data that is involved. As such,many tasks that really on data processing, storage, or communicationsmay be better suited by platforms that include edge solutions such asthe storage systems described above.

Consider a specific example of inventory management in a warehouse,distribution center, or similar location. A large inventory,warehousing, shipping, order-fulfillment, manufacturing or otheroperation has a large amount of inventory on inventory shelves, and highresolution digital cameras that produce a firehose of large data. All ofthis data may be taken into an image processing system, which may reducethe amount of data to a firehose of small data. All of the small datamay be stored on-premises in storage. The on-premises storage, at theedge of the facility, may be coupled to the cloud, for external reports,real-time control and cloud storage. Inventory management may beperformed with the results of the image processing, so that inventorycan be tracked on the shelves and restocked, moved, shipped, modifiedwith new products, or discontinued/obsolescent products deleted, etc.The above scenario is a prime candidate for an embodiment of theconfigurable processing and storage systems described above. Acombination of compute-only blades and offload blades suited for theimage processing, perhaps with deep learning on offload-FPGA oroffload-custom blade(s) could take in the firehose of large data fromall of the digital cameras, and produce the firehose of small data. Allof the small data could then be stored by storage nodes, operating withstorage units in whichever combination of types of storage blades besthandles the data flow. This is an example of storage and functionacceleration and integration. Depending on external communication needswith the cloud, and external processing in the cloud, and depending onreliability of network connections and cloud resources, the system couldbe sized for storage and compute management with bursty workloads andvariable conductivity reliability. Also, depending on other inventorymanagement aspects, the system could be configured for scheduling andresource management in a hybrid edge/cloud environment.

The storage systems described above may alone, or in combination withother computing resources, serves as a network edge platform thatcombines compute resources, storage resources, networking resources,cloud technologies and network virtualization technologies, and so on.As part of the network, the edge may take on characteristics similar toother network facilities, from the customer premise and backhaulaggregation facilities to Points of Presence (PoPs) and regional datacenters. Readers will appreciate that network workloads, such as VirtualNetwork Functions (VNFs) and others, will reside on the network edgeplatform. Enabled by a combination of containers and virtual machines,the network edge platform may rely on controllers and schedulers thatare no longer geographically co-located with the data processingresources. The functions, as microservices, may split into controlplanes, user and data planes, or even state machines, allowing forindependent optimization and scaling techniques to be applied. Such userand data planes may be enabled through increased accelerators, boththose residing in server platforms, such as FPGAs and Smart NICs, andthrough SDN-enabled merchant silicon and programmable ASICs.

The storage systems described above may also be optimized for use in bigdata analytics. Big data analytics may be generally described as theprocess of examining large and varied data sets to uncover hiddenpatterns, unknown correlations, market trends, customer preferences andother useful information that can help organizations make more-informedbusiness decisions. Big data analytics applications enable datascientists, predictive modelers, statisticians and other analyticsprofessionals to analyze growing volumes of structured transaction data,plus other forms of data that are often left untapped by conventionalbusiness intelligence (BI) and analytics programs. As part of thatprocess, semi-structured and unstructured data such as, for example,internet clickstream data, web server logs, social media content, textfrom customer emails and survey responses, mobile-phone call-detailrecords, IoT sensor data, and other data may be converted to astructured form. Big data analytics is a form of advanced analytics,which involves complex applications with elements such as predictivemodels, statistical algorithms and what-if analyses powered by analyticssystems.

The storage systems described above may also support (includingimplementing as a system interface) applications that perform tasks inresponse to human speech. For example, the storage systems may supportthe execution intelligent personal assistant applications such as, forexample, Amazon's Alexa, Apple Siri, Google Voice, Samsung Bixby,Microsoft Cortana, and others. While the examples described in theprevious sentence make use of voice as input, the storage systemsdescribed above may also support chatbots, talkbots, chatterbots, orartificial conversational entities or other applications that areconfigured to conduct a conversation via auditory or textual methods.Likewise, the storage system may actually execute such an application toenable a user such as a system administrator to interact with thestorage system via speech. Such applications are generally capable ofvoice interaction, music playback, making to-do lists, setting alarms,streaming podcasts, playing audiobooks, and providing weather, traffic,and other real time information, such as news, although in embodimentsin accordance with the present disclosure, such applications may beutilized as interfaces to various system operations.

The storage systems described above may also implement AI platforms fordelivering on the vision of self-driving storage. Such AI platforms maybe configured to deliver global predictive intelligence by collectingand analyzing large amounts of storage system telemetry data points toenable effortless management, analytics and support. In fact, suchstorage systems may be capable of predicting both capacity andperformance, as well as generating intelligent advice on workloaddeployment, interaction and optimization. Such AI platforms may beconfigured to scan all incoming storage system telemetry data against alibrary of issue fingerprints to predict and resolve incidents inreal-time, before they impact customer environments, and captureshundreds of variables related to performance that are used to forecastperformance load.

The storage systems described above may support the serialized orsimultaneous execution artificial intelligence applications, machinelearning applications, data analytics applications, datatransformations, and other tasks that collectively may form an AIladder. Such an AI ladder may effectively be formed by combining suchelements to form a complete data science pipeline, where existdependencies between elements of the AI ladder. For example, AI mayrequire that some form of machine learning has taken place, machinelearning may require that some form of analytics has taken place,analytics may require that some form of data and informationarchitecting has taken place, and so on. As such, each element may beviewed as a rung in an AI ladder that collectively can form a completeand sophisticated AI solution.

The storage systems described above may also, either alone or incombination with other computing environments, be used to deliver an AIeverywhere experience where AI permeates wide and expansive aspects ofbusiness and life. For example, AI may play an important role in thedelivery of deep learning solutions, deep reinforcement learningsolutions, artificial general intelligence solutions, autonomousvehicles, cognitive computing solutions, commercial UAVs or drones,conversational user interfaces, enterprise taxonomies, ontologymanagement solutions, machine learning solutions, smart dust, smartrobots, smart workplaces, and many others. The storage systems describedabove may also, either alone or in combination with other computingenvironments, be used to deliver a wide range of transparently immersiveexperiences where technology can introduce transparency between people,businesses, and things. Such transparently immersive experiences may bedelivered as augmented reality technologies, connected homes, virtualreality technologies, brain-computer interfaces, human augmentationtechnologies, nanotube electronics, volumetric displays, 4D printingtechnologies, or others. The storage systems described above may also,either alone or in combination with other computing environments, beused to support a wide variety of digital platforms. Such digitalplatforms can include, for example, 5G wireless systems and platforms,digital twin platforms, edge computing platforms, IoT platforms, quantumcomputing platforms, serverless PaaS, software-defined security,neuromorphic computing platforms, and so on.

Readers will appreciate that some transparently immersive experiencesmay involve the use of digital twins of various “things” such as people,places, processes, systems, and so on. Such digital twins and otherimmersive technologies can alter the way that humans interact withtechnology, as conversational platforms, augmented reality, virtualreality and mixed reality provide a more natural and immersiveinteraction with the digital world. In fact, digital twins may be linkedwith the real-world, perhaps even in real-time, to understand the stateof a thing or system, respond to changes, and so on. Because digitaltwins consolidate massive amounts of information on individual assetsand groups of assets (even possibly providing control of those assets),digital twins may communicate with each other to digital factory modelsof multiple linked digital twins.

The storage systems described above may also be part of a multi-cloudenvironment in which multiple cloud computing and storage services aredeployed in a single heterogeneous architecture. In order to facilitatethe operation of such a multi-cloud environment, DevOps tools may bedeployed to enable orchestration across clouds. Likewise, continuousdevelopment and continuous integration tools may be deployed tostandardize processes around continuous integration and delivery, newfeature rollout and provisioning cloud workloads. By standardizing theseprocesses, a multi-cloud strategy may be implemented that enables theutilization of the best provider for each workload. Furthermore,application monitoring and visibility tools may be deployed to moveapplication workloads around different clouds, identify performanceissues, and perform other tasks. In addition, security and compliancetools may be deployed for to ensure compliance with securityrequirements, government regulations, and so on. Such a multi-cloudenvironment may also include tools for application delivery and smartworkload management to ensure efficient application delivery and helpdirect workloads across the distributed and heterogeneousinfrastructure, as well as tools that ease the deployment andmaintenance of packaged and custom applications in the cloud and enableportability amongst clouds. The multi-cloud environment may similarlyinclude tools for data portability.

The storage systems described above may be used as a part of a platformto enable the use of crypto-anchors that may be used to authenticate aproduct's origins and contents to ensure that it matches a blockchainrecord associated with the product. Such crypto-anchors may take manyforms including, for example, as edible ink, as a mobile sensor, as amicrochip, and others. Similarly, as part of a suite of tools to securedata stored on the storage system, the storage systems described abovemay implement various encryption technologies and schemes, includinglattice cryptography. Lattice cryptography can involve constructions ofcryptographic primitives that involve lattices, either in theconstruction itself or in the security proof. Unlike public-key schemessuch as the RSA, Diffie-Hellman or Elliptic-Curve cryptosystems, whichare easily attacked by a quantum computer, some lattice-basedconstructions appear to be resistant to attack by both classical andquantum computers.

A quantum computer is a device that performs quantum computing. Quantumcomputing is computing using quantum-mechanical phenomena, such assuperposition and entanglement. Quantum computers differ fromtraditional computers that are based on transistors, as such traditionalcomputers require that data be encoded into binary digits (bits), eachof which is always in one of two definite states (0 or 1). In contrastto traditional computers, quantum computers use quantum bits, which canbe in superpositions of states. A quantum computer maintains a sequenceof qubits, where a single qubit can represent a one, a zero, or anyquantum superposition of those two qubit states. A pair of qubits can bein any quantum superposition of 4 states, and three qubits in anysuperposition of 8 states. A quantum computer with n qubits cangenerally be in an arbitrary superposition of up to 2{circumflex over( )}n different states simultaneously, whereas a traditional computercan only be in one of these states at any one time. A quantum Turingmachine is a theoretical model of such a computer.

The storage systems described above may also be paired withFPGA-accelerated servers as part of a larger AI or ML infrastructure.Such FPGA-accelerated servers may reside near (e.g., in the same datacenter) the storage systems described above or even incorporated into anappliance that includes one or more storage systems, one or moreFPGA-accelerated servers, networking infrastructure that supportscommunications between the one or more storage systems and the one ormore FPGA-accelerated servers, as well as other hardware and softwarecomponents. Alternatively, FPGA-accelerated servers may reside within acloud computing environment that may be used to perform compute-relatedtasks for AI and ML jobs. Any of the embodiments described above may beused to collectively serve as a FPGA-based AI or ML platform. Readerswill appreciate that, in some embodiments of the FPGA-based AI or MLplatform, the FPGAs that are contained within the FPGA-acceleratedservers may be reconfigured for different types of ML models (e.g.,LSTMs, CNNs, GRUs). The ability to reconfigure the FPGAs that arecontained within the FPGA-accelerated servers may enable theacceleration of a ML or AI application based on the most optimalnumerical precision and memory model being used. Readers will appreciatethat by treating the collection of FPGA-accelerated servers as a pool ofFPGAs, any CPU in the data center may utilize the pool of FPGAs as ashared hardware microservice, rather than limiting a server to dedicatedaccelerators plugged into it.

The FPGA-accelerated servers and the GPU-accelerated servers describedabove may implement a model of computing where, rather than keeping asmall amount of data in a CPU and running a long stream of instructionsover it as occurred in more traditional computing models, the machinelearning model and parameters are pinned into the high-bandwidth on-chipmemory with lots of data streaming though the high-bandwidth on-chipmemory. FPGAs may even be more efficient than GPUs for this computingmodel, as the FPGAs can be programmed with only the instructions neededto run this kind of computing model.

The storage systems described above may be configured to provideparallel storage, for example, through the use of a parallel file systemsuch as BeeGFS. Such parallel files systems may include a distributedmetadata architecture. For example, the parallel file system may includea plurality of metadata servers across which metadata is distributed, aswell as components that include services for clients and storageservers. Through the use of a parallel file system, file contents may bedistributed over a plurality of storage servers using striping andmetadata may be distributed over a plurality of metadata servers on adirectory level, with each server storing a part of the complete filesystem tree. Readers will appreciate that in some embodiments, thestorage servers and metadata servers may run in userspace on top of anexisting local file system. Furthermore, dedicated hardware is notrequired for client services, the metadata servers, or the hardwareservers as metadata servers, storage servers, and even the clientservices may be run on the same machines.

Readers will appreciate that, in part due to the emergence of many ofthe technologies discussed above including mobile devices, cloudservices, social networks, big data analytics, and so on, an informationtechnology platform may be needed to integrate all of these technologiesand drive new business opportunities by quickly deliveringrevenue-generating products, services, and experiences—rather thanmerely providing the technology to automate internal business processes.Information technology organizations may need to balance resources andinvestments needed to keep core legacy systems up and running while alsointegrating technologies to build an information technology platformthat can provide the speed and flexibility in areas such as, forexample, exploiting big data, managing unstructured data, and workingwith cloud applications and services. One possible embodiment of such aninformation technology platform is a composable infrastructure thatincludes fluid resource pools, such as many of the systems describedabove that, can meet the changing needs of applications by allowing forthe composition and recomposition of blocks of disaggregated compute,storage, and fabric infrastructure. Such a composable infrastructure canalso include a single management interface to eliminate complexity and aunified API to discover, search, inventory, configure, provision,update, and diagnose the composable infrastructure.

The systems described above can support the execution of a wide array ofsoftware applications. Such software applications can be deployed in avariety of ways, including container-based deployment models.Containerized applications may be managed using a variety of tools. Forexample, containerized applications may be managed using Docker Swarm, aclustering and scheduling tool for Docker containers that enables ITadministrators and developers to establish and manage a cluster ofDocker nodes as a single virtual system. Likewise, containerizedapplications may be managed through the use of Kubernetes, acontainer-orchestration system for automating deployment, scaling andmanagement of containerized applications. Kubernetes may execute on topof operating systems such as, for example, Red Hat Enterprise Linux,Ubuntu Server, SUSE Linux Enterprise Servers, and others. In suchexamples, a master node may assign tasks to worker/minion nodes.Kubernetes can include a set of components (e.g., kubelet, kube-proxy,cAdvisor) that manage individual nodes as a well as a set of components(e.g., etcd, API server, Scheduler, Control Manager) that form a controlplane. Various controllers (e.g., Replication Controller, DaemonSetController) can drive the state of a Kubernetes cluster by managing aset of pods that includes one or more containers that are deployed on asingle node. Containerized applications may be used to facilitate aserverless, cloud native computing deployment and management model forsoftware applications. In support of a serverless, cloud nativecomputing deployment and management model for software applications,containers may be used as part of an event handling mechanisms (e.g.,AWS Lambdas) such that various events cause a containerized applicationto be spun up to operate as an event handler.

The systems described above may be deployed in a variety of ways,including being deployed in ways that support fifth generation (‘5G’)networks. 5G networks may support substantially faster datacommunications than previous generations of mobile communicationsnetworks and, as a consequence may lead to the disaggregation of dataand computing resources as modern massive data centers may become lessprominent and may be replaced, for example, by more-local, micro datacenters that are close to the mobile-network towers. The systemsdescribed above may be included in such local, micro data centers andmay be part of or paired to multi-access edge computing (‘MEC’) systems.Such MEC systems may enable cloud computing capabilities and an ITservice environment at the edge of the cellular network. By runningapplications and performing related processing tasks closer to thecellular customer, network congestion may be reduced and applicationsmay perform better. MEC technology is designed to be implemented at thecellular base stations or other edge nodes, and enables flexible andrapid deployment of new applications and services for customers. MEC mayalso allow cellular operators to open their radio access network (‘RAN’)to authorized third-parties, such as application developers and contentprovider. Furthermore, edge computing and micro data centers maysubstantially reduce the cost of smartphones that work with the 5Gnetwork because customer may not need devices with such processing powerand the requisite components.

Readers will appreciate that 5G networks may generate more data thanprevious network generations, especially in view of the fact that thehigh network bandwidth offered by 5G networks may cause the 5G networksto handle amounts and types of data (e.g., sensor data from self-drivingcars, data generated by AR/VR technologies) that weren't as feasible forprevious generation networks. In such examples, the scalability offeredby the systems described above may be very valuable as the amount ofdata increases, adoption of emerging technologies increase, and so on.

For further explanation, FIG. 3E illustrates an exemplary computingdevice 350 that may be specifically configured to perform one or more ofthe processes described herein. As shown in FIG. 3E, computing device350 may include a communication interface 352, a processor 354, astorage device 356, and an input/output (“I/O”) module 358communicatively connected one to another via a communicationinfrastructure 360. While an exemplary computing device 350 is shown inFIG. 3E, the components illustrated in FIG. 3E are not intended to belimiting. Additional or alternative components may be used in someembodiments. Components of computing device 350 shown in FIG. 3E willnow be described in additional detail.

Communication interface 352 may be configured to communicate with one ormore computing devices. Examples of communication interface 352 include,without limitation, a wired network interface (such as a networkinterface card), a wireless network interface (such as a wirelessnetwork interface card), a modem, an audio/video connection, and anyother interface.

Processor 354 generally represents any type or form of processing unitcapable of processing data and/or interpreting, executing, and/ordirecting execution of one or more of the instructions, processes,and/or operations described herein. Processor 354 may perform operationsby executing computer-executable instructions 362 (e.g., an application,software, code, and/or other executable data instance) stored in storagedevice 356.

Storage device 356 may include one or more data storage media, devices,or configurations and may employ any type, form, and combination of datastorage media and/or device. For example, storage device 356 mayinclude, but is not limited to, any combination of the non-volatilemedia and/or volatile media described herein. Electronic data, includingdata described herein, may be temporarily and/or permanently stored instorage device 356. For example, data representative ofcomputer-executable instructions 362 configured to direct processor 354to perform any of the operations described herein may be stored withinstorage device 356. In some examples, data may be arranged in one ormore databases residing within storage device 356.

I/O module 358 may include one or more I/O modules configured to receiveuser input and provide user output. I/O module 358 may include anyhardware, firmware, software, or combination thereof supportive of inputand output capabilities. For example, I/O module 358 may includehardware and/or software for capturing user input, including, but notlimited to, a keyboard or keypad, a touchscreen component (e.g.,touchscreen display), a receiver (e.g., an RF or infrared receiver),motion sensors, and/or one or more input buttons.

I/O module 358 may include one or more devices for presenting output toa user, including, but not limited to, a graphics engine, a display(e.g., a display screen), one or more output drivers (e.g., displaydrivers), one or more audio speakers, and one or more audio drivers. Incertain embodiments, I/O module 358 is configured to provide graphicaldata to a display for presentation to a user. The graphical data may berepresentative of one or more graphical user interfaces and/or any othergraphical content as may serve a particular implementation. In someexamples, any of the systems, computing devices, and/or other componentsdescribed herein may be implemented by computing device 350.

For further explanation, FIG. 4 sets forth a flow chart illustrating anexample of providing application-specific storage by a storage system(406) in accordance with some embodiments of the present disclosure.Although depicted in less detail, the storage system (406) depicted inFIG. 4 may be similar to the storage systems described above. In fact,the storage system (406) depicted in FIG. 4 may be embodied as one ofthe storage systems described above, as a combination of a plurality ofthe storage systems described above, as a modified version of one of thestorage systems described above, as a combination of a plurality ofmodified versions of the storage systems described above, or anycombination thereof. As such, the storage system (406) depicted in FIG.4 includes a plurality of storage subsystems (412 a, 412 b, 412 c, 412n). Each of the storage subsystems (412 a, 412 b, 412 c, 412 n) depictedin FIG. 4 may be embodied, for example, as one or more storage devices,as one or more of the storage systems described above, as modifiedversions of the storage systems described above, and so on.

The example method depicted in FIG. 4 also includes an application (404)that is executing on a host (402). The application (404) may beembodied, for example, as one or more modules of computer programinstructions that are executing, directly or indirectly, on computerhardware such as a CPU. In the example method depicted in FIG. 4 , theapplication (404) may be configured to utilized storage within thestorage system (406), for example, by issuing a request to read datafrom the storage system (406), by issuing a request to write data to thestorage system (406), and so on. In the example method depicted in FIG.4 , the application (404) is executing on a host (402). The host (402)may be embodied, for example, as a server that is coupled to the storagesystem (406) via a data communications network such as a SAN. Inalternative embodiments, the application (404) may be executingelsewhere. For example, the application (402) may be executing oncomputer hardware that is included within the storage system (406)itself, the application (402) may be executing in a cloud computingenvironment, and so on.

The example method depicted in FIG. 4 can include identifying (408), forthe application (404), one or more characteristics associated with theapplication (404). The one or more characteristics associated with theapplication (404) may be used to identify how the storage system (406)can best provide storage resources to the application (404). The one ormore characteristics associated with the application (404) may includeinformation such as, for example, the type of storage (e.g., block,file, object) that the application (404) will access, the amount of readbandwidth that needs to be provided to the application (404), the amountof write bandwidth that needs to be provided to the application (404),information describing service level agreements that the application(404) must adhere to, information describing quality-of-service (‘QoS’)requirements associated with the application, and many more. In such anexample, because the storage system (406) can include a large pool ofdisparate resources, the one or more characteristics associated with theapplication (404) can be used to determine which particular storagesubsystems (412 a, 412 b, 412 c, 412 n) should be used to providestorage to the application (404).

Consider an example in which a first storage subsystem (412 a) withinthe storage system (406) is embodied as a storage array that providesblock storage to users of the first storage subsystem (412 a) and asecond storage subsystem (412 b) within the storage system (406) isembodied as a storage array that provides object storage to users of thesecond storage subsystem (412 b). In such an example, if the one or morecharacteristics associated with the application (404) indicates that theapplication (404) uses block storage, the storage system (406) may makeone or more volumes on the first storage subsystem (412 a) available tothe application (404). Alternatively, if the one or more characteristicsassociated with the application (404) indicates that the application(404) uses object storage, the storage system (406) may make objectstorage pools on the second storage subsystem (412 b) available to theapplication (404).

Consider an additional example in which a first storage subsystem (412a) within the storage system (406) is embodied as a high performancestorage array that can provide relatively low read latencies, relativelylow write latencies, and a relatively large number of IOPS and a secondstorage subsystem (412 b) within the storage system (406) is embodied asa lower performance storage array that provides relatively high readlatencies, relatively high write latencies, and a relatively smallnumber of IOPS. In such an example, if the one or more characteristicsassociated with the application (404) indicates that the application(404) needs to service a relatively large number of IOPS in a way thatis latency sensitive, the storage system (406) may be configured toplace data that is accessed by the application (404) on the firststorage array. Alternatively, if the one or more characteristicsassociated with the application (404) indicates that the application(404) only needs to service a relatively small number of IOPS in a waythat is not particularly latency sensitive, the storage system (406) maybe configured to place data that is accessed by the application (404) onthe second storage array and reserve the higher performance storageresources for applications that demand a higher level of performancefrom the underlying storage.

In the example method depicted in FIG. 4 , the one or morecharacteristics associated with the application (404) may be identified(408) in a variety of ways. In one embodiment, for example, the one ormore characteristics associated with the application (404) may beprovided to the storage system (406) via a user interface. In such anexample, a system administrator or other authorized user may have accessto an interface that allows them to specify things like the amount ofread bandwidth needed to support the application (404), the number ofIOPS that need to be serviced by the application (404), and so on. Inthis type of embodiment, one or more characteristics associated with theapplication (404) may be explicitly provided by a user.

In an alternative embodiment, the one or more characteristics associatedwith the application (404) may be provided to the storage system (406)in a less explicit way. For example, a system administrator or otherauthorized user may have access to an interface that includes tunableelements (e.g., knobs) that allows them to specify things like arelative amount of performance that is needed by the application, arelative amount of data protection that is required by the application,a relative amount of data security that is required by the application,data compliance requirements for data that is associated with theapplication, and so on. In such an example, each knob may be presentedvia a graphical user interface and the user may set each knob to a valuebetween 1-10, where each combination of settings maps to the underlyingstorage resources that can provide the application with the best matchto the combination of settings. Similarly, users may be presented withvarious classes of storage to select from, where examples of suchstorage classes can include ‘low performance, low security’ storage,‘high performance, low security’ storage, ‘low performance, highsecurity’ storage, ‘high performance, high security’ storage, and so on,including classes that have fewer, additional, or other descriptors. Inthis type of embodiment, one or more characteristics associated with theapplication (404) may be inferred by the storage system (406) based oninput provided by a user.

In an alternative embodiment, the one or more characteristics associatedwith the application (404) may be provided to the storage system (406)not by user input via a user interface, but rather through the use ofAPIs and similar tools. For example, an application developer may haveaccess to an API that allows the software developer to specify thingslike the type of application that the developer has developed, QoSaspects requested by the developer, security related aspects requestedby the developer, performance related aspects requested by thedeveloper, and so on. In this type of embodiment, one or morecharacteristics associated with the application (404) may be gathered bythe storage system (406) by providing developers and systemadministrators with tools that enable the developers and systemadministrators to provide such information to the storage system (406).

In another alternative embodiment, the one or more characteristicsassociated with the application (404) may be provided to the storagesystem (406) via information that describes the application (404). Theinformation that describes the application (404) may be included inmetadata that describes the application, the information that describesthe application (404) may be provided from a user such as theapplication developer, the information that describes the application(404) may be extracted from a service level agreement or similardescription of the QoS required by the application (404), and in otherways. In this type of embodiment, the one or more characteristicsassociated with the application (404) may be extracted from orextrapolated from the information that describes the application (404).

Consider an example in which a first storage subsystem (412 a) withinthe storage system (406) is embodied as a converged infrastructure inwhich multiple components (e.g., servers, data storage devices,networking equipment, software components, and so on) are grouped into asingle, optimized computing package whereas a second storage subsystem(412 b) within the storage system (406) is embodied as a hyper-convergedinfrastructure with software-defined components that virtualizes all ofthe elements of conventional hardware-defined systems. In such anexample, some types of applications may perform better when using ahyper-converged infrastructure whereas other types of applications mayperform better when utilizing a converged infrastructure. In such anexample, the information that describes the application (404) may beused to identify a general categorization for the application (e.g., theapplication is a virtualized desktop application, the application is adatabase application) and the storage system (406) can use thisinformation to support the application on the storage subsystem thatprovides better support for that type of application.

In an alternative embodiment, the one or more characteristics associatedwith the application (404) may be provided to the storage system (406)via monitoring the execution of the application (404). Monitoring theexecution of the application (404) may be carried out, for example, bymonitoring the application (404) to generate application levelstatistics for the application (404) such as, for example, the amount ofwrite bandwidth generated by the application, the amount of readbandwidth generated by the application, the number of IOPS generated bythe application, and so on. Likewise, the I/O patterns of theapplication (404) may be monitored and compared to fingerprints forknown types of applications. For example, a database application maytend to generate I/O in a first pattern (e.g., random reads) whereas alog analysis application may tend to generate I/O in a second pattern(e.g., sequential reads). In such an example, when the I/O patterns of aparticular application (404) match up reasonably well with fingerprintsfor known types of applications, the storage system (406) may assumethat the one or more characteristics associated with the application(404) are similar to similar applications. In this type of embodiment,the one or more characteristics associated with the application (404)may be generated by observing the actual execution of the application(404).

Readers will appreciate that identifying (408) one or morecharacteristics associated with the application (404) may be carried outin additional ways and that some characteristics associated with theapplication (404) may be identified (408) in one way while othercharacteristics associated with the application (404) may be identified(408) in another way. For example, some characteristics associated withthe application (404) may be identified (408) by observing the actualexecution of the application (404) whereas other characteristicsassociated with the application (404) may be explicitly provided by auser.

The example method depicted in FIG. 4 also includes selecting (410), independence upon the one or more characteristics associated with theapplication (404) and characteristics of storage resources within thestorage system (406), one or more storage resources within the storagesystem (406) to support the execution of the application (404).Selecting (410) one or more storage resources within the storage system(406) to support the execution of the application (404) may be carriedout, for example, by comparing the one or more characteristicsassociated with the application (404) and the characteristics of storageresources within the storage system (406) to identify a best fit. Insuch an example, multiple aspects of the one or more characteristicsassociated with the application (404) and the characteristics of storageresources within the storage system (406) may be taken intoconsideration, in a weighted or unweighted fashion, to identify the setof storage resources that would provide the best fit for the application(404) given the characteristics associated with the application (404).

For further explanation, FIG. 5 sets forth a flow chart illustrating anadditional example method of providing application-specific storage by astorage system (406) in accordance with some embodiments of the presentdisclosure. The example method depicted in FIG. 5 is similar to theexample method depicted in FIG. 4 , as the example method depicted inFIG. 5 also includes identifying (408) one or more characteristicsassociated with an application (404) and selecting (410) one or morestorage resources within the storage system (406) to support theexecution of the application (404) in dependence upon the one or morecharacteristics associated with the application (404) andcharacteristics of storage resources within the storage system (406).

In the example method depicted in FIG. 5 , the storage system (406)supports the execution of a plurality of applications (404, 504). Thestorage system (406) may support the execution of a plurality ofapplications (404, 504), for example, by serving as data storageresources that may be used by the applications (404, 504), by executingone or more modules of computer program instructions that form theapplications (404, 504), or in some other way. In the example depictedin FIG. 5 , a second application (504) is illustrated as being executedon a second host (502), which may be coupled to the storage system (406)via one or more data communications networks.

In the example method depicted in FIG. 5 , for each application (404,504), the storage resources within the storage system (406) that supportthe execution of the application (404, 504) are located in distinctapplication isolation domains (506, 508). Each application isolationdomain (506, 508) depicted in FIG. 5 may be embodied as one or moremechanisms used to isolate executed software applications from oneanother so that they do not affect each other. In the example methoddepicted in FIG. 5 , each application isolation domain (506, 508) may beused to segment portions of the storage system (506, 508), such thatdifferent policies may be applied to the segmented portions of thestorage system (506, 508). For example, a first application isolationdomain (506) may be used to support the execution of a first application(404) and may be associated with a first data protection policy, a firstdata security policy, a first set of performance policies, and so on.Likewise, a second application isolation domain (508) may be used tosupport the execution of a second application (504) and may beassociated with a second data protection policy, a second data securitypolicy, a second set of performance policies, and so on.

Readers will appreciate that, through the use of application isolationdomains (506, 508), data for a particular application may be managed ina way that is most appropriate for that particular application. Forexample, if a first application (404) has relatively tight recoverypoint objectives, the application isolation domain (506) that is used tosupport the application (404) may replicate (i.e. backup) datarelatively frequently. Alternatively, if a second application (504) hasrelatively relaxed recovery point objectives, the application isolationdomain (508) that is used to support the application (504) may replicate(i.e. backup) data relatively infrequently. Readers will appreciate thatother aspects of data management may also be carried out differentlyacross different application isolation domains (506, 508).

Readers will further appreciate that, through the use of applicationisolation domains (506, 508), the extent to which a particularapplication consumes system resources may also be managed. Consider anexample in which multiple application isolation domains exist within asingle storage subsystem (e.g., a single storage array) within thestorage system (406). In such an example, the extent to which oneapplication consumes resources can impact the extent to which anotherapplication has access to system resources. For example, if a firstapplication (404) begins generating very large amount of I/O operations,a second application (504) may experience a delay in having its I/Ooperations serviced as each application may route its I/O operationsthrough the same set of storage controllers within the storagesubsystem. In order to prevent such a situation from occurring, thestorage system (406) may takes steps such as throttling the applicationisolation domain (506) that supports the execution of the firstapplication (404) such that only a predetermined amount of IOPS, readbandwidth, write bandwidth, or some other quantifiable measure of totalsystem resources for the storage subsystem that supports eachapplication isolation domain (506, 508) is made available to theapplication isolation domain (506) that is being throttled.

For further explanation, FIG. 6 sets forth a flow chart illustrating anadditional example method of providing application-specific storage by astorage system (406) in accordance with some embodiments of the presentdisclosure. The example method depicted in FIG. 6 is similar to theexample methods depicted in FIG. 4 and FIG. 5 , as the example methoddepicted in FIG. 6 also includes identifying (408) one or morecharacteristics associated with an application (404) and selecting (410)one or more storage resources within the storage system (406) to supportthe execution of the application (404) in dependence upon the one ormore characteristics associated with the application (404) andcharacteristics of storage resources within the storage system (406).

As described in greater detail above, storage resources within thestorage system (406) are selected (410) to support the execution of theapplication (404) in dependence upon the one or more characteristicsassociated with the application (404) and characteristics of storageresources within the storage system (406). Readers will appreciate that,over time, the characteristics associated with the application (404) maychange. For example, if the application is embodied as a web portal fora particular retailer, the retailer may expand its customer base througheffective advertising, the delivery of superior products, or for avariety of reasons. In such an example, the characteristics associatedwith the application (404) may also change due to an increase in trafficto the retailer's website. Readers will also appreciate that, over time,the characteristics of storage resources within the storage system (406)may also change. For example, older components (e.g., storage devices)in a particular storage subsystem may be replaced with newer components,components within a particular storage subsystem may age and fail,additional components may be added within a particular storagesubsystem, additional storage subsystems may be added to the storagesystem (406), and so on. Because the one or more characteristicsassociated with the application (404) and the characteristics of storageresources within the storage system (406) are not static, as eithercharacteristic set changes, the storage resources within the storagesystem (406) that would be selected (410) to support the execution ofthe application (404) may change as well. As such, embodiments of thepresent disclosure can include periodically (or even in response to someevent) selecting (410) one or more storage resources within the storagesystem (406) to support the execution of the application (404) independence upon the one or more characteristics associated with theapplication (404) and characteristics of storage resources within thestorage system (406).

In the example method depicted in FIG. 6 , when one iteration ofselecting (410) one or more storage resources within the storage system(406) to support the execution of the application (404) in dependenceupon the one or more characteristics associated with the application(404) and characteristics of storage resources within the storage system(406) yields a different result that a previous iteration of selecting(410) one or more storage resources within the storage system (406) tosupport the execution of the application (404) in dependence upon theone or more characteristics associated with the application (404) andcharacteristics of storage resources within the storage system (406),various actions may be taken by the storage system (406).

One example of an action that may be taken by the storage system (406)in response to a different outcome being generated when selecting (410)one or more storage resources within the storage system (406) to supportthe execution of the application (404) is to generate (602) arecommendation to cease supporting the execution of the application(404) on first storage resources within the storage system (406) andbegin supporting the execution of the application (404) on secondstorage resources within the storage system (406). Such a recommendationmay be presented, for example, to a user such as a system administratoror application developer such that the user can choose whether to acceptthe recommendation. In such an example, if the user decides to acceptthe recommendation, a process may be initiated to move data, modify datacommunications connections, and so on, to enable the application (404)to use the updated set of storage resources within the storage system(406). For example, if the storage system (406) initially selected (410)a first storage subsystem (412 a) to support the execution of the firstapplication (404), but the storage system (406) subsequently selected(410) a second storage subsystem (412 b) to support the execution of thefirst application (404), data associated with the first application(404) may be migrated from the first storage subsystem (412 a) to thesecond storage subsystem (412 b), connection information may be updatedsuch that I/Os issued by the first storage subsystem (412 a), and so on.

An additional example of an action that may be taken by the storagesystem (406) in response to a different outcome being generated whenselecting (410) one or more storage resources within the storage system(406) to support the execution of the application (404) is for thestorage system (406) to cease (604) supporting the execution of theapplication (404) on first storage resources within the storage system(406) and begin supporting (606) the execution of the application (404)on second storage resources within the storage system (406). In such anexample, a process may be initiated to move data, modify datacommunications connections, and so on, to enable the application (404)to use the updated set of storage resources within the storage system(406). Readers will appreciate that the two examples described aboverepresent differing levels of automation that may be implemented andother embodiments are possible.

For further explanation, FIG. 7 sets forth a flow chart illustrating anadditional example method of providing application-specific storage by astorage system (406) in accordance with some embodiments of the presentdisclosure. The example method depicted in FIG. 7 is similar to theexample methods depicted in FIGS. 4-6 , as the example method depictedin FIG. 7 also includes identifying (408) one or more characteristicsassociated with an application (404) and selecting (410) one or morestorage resources within the storage system (406) to support theexecution of the application (404) in dependence upon the one or morecharacteristics associated with the application (404) andcharacteristics of storage resources within the storage system (406).

The example method depicted in FIG. 7 also includes predicting (702) achange to the application (404). Predicting (702) a change to theapplication may be carried out, for example, by detecting that theapplication itself has changed and even by projecting a change to theapplication over time. In such an example, projecting a change to theapplication (404) over time may be carried out by performing atime-series analysis of various performance metrics associated with theapplication (404) to identify trends associated with the application(404). In addition, projecting a change to the application (404) overtime may also be carried out based on telemetry data collected forsimilar applications as the telemetry data may be useful for identifyingtrends associated with the application (404). As such, an examination ofthe various performance metrics associated with the application (404)and the telemetry data can be used to generate trending information forthe applications (404) including, for example, information describingthe rate at which the number of IOPS being generated by the application(404) has been changing, the rate at which overwrite rates for I/Ooperations that are being generated by the application (404) arechanging, the rate at which the amount of read bandwidth that is beingconsumed by I/O operations generated by the application (404) ischanging, and many others. In such a way, predicted characteristics(416) of the application (404) may be generated by extrapolatingidentified trends out over a period of time in the future.

Consider an example in which telemetry data gathered from a plurality ofstorage systems indicates that, on average, the amount of CPU resourcesrequired to support a virtual desktop infrastructure workload doublesevery three years. In such an example, if a particular application (404)is a virtual desktop infrastructure workload, predicting (702) a changeto the application (404) may be carried out, at least in part, bydetermining the amount of CPU resources currently required to supportthe application (404) and assuming that the amount of CPU resources thatwill be required to support the application (404) in the future willdouble every three years. In such a way, the load demands created byeach application (404) may be projected to some point in the future.

The example method depicted in FIG. 7 also includes identifying (704),in dependence upon the predicted change to the application (404), one ormore updated characteristics associated with the application (404).Identifying (704) one or more updated characteristics associated withthe application (404) may be carried out, for example, by utilizingtrending information associated with the application (404) itself,trending information associated with the application (404) itself andtrending information based on telemetry data associated with similarapplications, or some combination thereof, to project how theapplication (404) will operate in the future.

Readers will appreciate that embodiments described herein may alsoleverage machine learning techniques to not only predicting (702) achange to the application (404) but also to identify (704) one or moreupdated characteristics associated with the application (404). In suchan example, various metrics associated with the application (404) andeven similar applications may be fed into a machine learning model todetect the occurrence of changes to the application (404), changes tothe application's (404) usage of underlying storage resources, and soon, to gain a better understanding of characteristics that the workloadmay take on in the future.

The example method depicted in FIG. 7 also includes selecting (706), independence upon the one or more updated characteristics associated withthe application (404) and characteristics of storage resources withinthe storage system (406), one or more updated storage resources withinthe storage system to support the execution of the application (404).Selecting (706) one or more storage resources within the storage system(406) to support the execution of the application (404) may be carriedout, for example, by comparing the one or more updated characteristicsassociated with the application (404) and the characteristics of storageresources within the storage system (406) to identify a best fit. Insuch an example, multiple aspects of the one or more updatedcharacteristics associated with the application (404) and thecharacteristics of storage resources within the storage system (406) maybe taken into consideration, in a weighted or unweighted fashion, toidentify the set of storage resources that would provide the best fitfor the application (404) given the updated characteristics of theapplication (404).

For further explanation, FIG. 8 sets forth a flow chart illustrating anadditional example method of providing application-specific storage by astorage system (406) in accordance with some embodiments of the presentdisclosure. The example method depicted in FIG. 8 is similar to theexample methods depicted in FIGS. 4-7 , as the example method depictedin FIG. 8 also includes identifying (408) one or more characteristicsassociated with an application (404) and selecting (410) one or morestorage resources within the storage system (406) to support theexecution of the application (404) in dependence upon the one or morecharacteristics associated with the application (404) andcharacteristics of storage resources within the storage system (406).

The example method depicted in FIG. 8 also includes predicting (802) achange to one or more resources within the storage system (406). Readerswill appreciate that one or more storage resources may exhibit differentbehavior, for example, as they age, as components are replaced orupgraded (including software components), as the workloads service by aset of resources change, and for many other reasons. In such an example,the expected changes to the storage resources may be utilized togenerate updated characteristics of storage resources within the storagesystem (406).

The example method depicted in FIG. 8 also includes selecting (804), independence upon the one or more characteristics associated with theapplication (404) and updated characteristics of storage resourceswithin the storage system (406), an updated set of storage resourceswithin the storage system (406) to support the execution of theapplication (404). Selecting (804) one or more storage resources withinthe storage system (406) to support the execution of the application(404) may be carried out, for example, by comparing the one or morecharacteristics associated with the application (404) and the updatedcharacteristics of storage resources within the storage system (406) toidentify a best fit. In such an example, multiple aspects of the one ormore characteristics associated with the application (404) and theupdated characteristics of storage resources within the storage system(406) may be taken into consideration, in a weighted or unweightedfashion, to identify the set of storage resources that would provide thebest fit for the application (404) given the updated characteristics ofstorage resources within the storage system (406).

For further explanation, FIG. 9 sets forth a flow chart illustrating anadditional example method of providing application-specific storage by astorage system (406) in accordance with some embodiments of the presentdisclosure. The example method depicted in FIG. 9 is similar to theexample methods depicted in FIGS. 4-8 , as the example method depictedin FIG. 9 also includes identifying (408) one or more characteristicsassociated with an application (404) and selecting (410) one or morestorage resources within the storage system (406) to support theexecution of the application (404) in dependence upon the one or morecharacteristics associated with the application (404) andcharacteristics of storage resources within the storage system (406).

The example method depicted in FIG. 9 also includes detecting (902) thatone or more storage resources within the storage system (406) willbecome constrained. Detecting (902) that one or more storage resourceswithin the storage system (406) will become constrained may be carriedout, for example, by detecting that the utilization of a particularresource either has or is projected to reach a predetermined utilizationlevel (e.g., 90% of full utilization). Consider the example describedabove in which multiple application isolation domains exist within asingle storage subsystem (e.g., a single storage array) within thestorage system (406). In such an example, the extent to which oneapplication consumes resources can impact the extent to which anotherapplication has access to system resources. For example, if a firstapplication (404) begins generating very large amount of I/O operations,a second application (504) may experience a delay in having its I/Ooperations serviced as each application may route its I/O operationsthrough the same set of storage controllers within the storagesubsystem. In such an example, detecting (902) that the storagecontrollers for the particular storage subsystem will become constrainedmay be carried out, for example, by detecting that the utilization ofthe storage controllers for the particular storage subsystem either hasor is projected to reach the predetermined utilization level.

The example method depicted in FIG. 9 also includes automatically (904),without user intervention, performing corrective actions. In the examplemethod depicted in FIG. 9 , the corrective actions may take a variety offorms. For example, automatically (904) performing corrective actionsmay include, to the extent possible, reconfiguring the storageresources. Consider an example in which the storage controllers for aparticular storage subsystem (412 a) are becoming constrained, where thestorage controllers are configured to perform inline deduplication andcompression of data as it comes into the storage subsystem (412 a) viarequests to write data that are issued by the applications that aresupported by the storage subsystem (412 a). In such an example,reconfiguring the storage resources may be carried out by reconfiguringthe storage controllers to not perform inline deduplication andcompression of data as it comes into the storage subsystem (412 a), butrather rely on the storage devices themselves to perform deduplicationoperations and compression operations on the data once it has beenwritten to non-volatile storage within the storage system (406) by thestorage controllers.

As another example of automatically (904) performing corrective actions,the storage system may effectively relocate workloads within the storagesystem (406). Consider an example in which multiple applications arebeing supported by a first storage subsystem (412 a) whose storagecontrollers are becoming constrained. In such an example, the storagesystem (406) may be configured to automatically migrate a dataset thatis associated with one or more of the applications that are beingsupported by the first storage subsystem (412 a) to a second storagesubsystem (412 b) (as well as updating connection information), suchthat I/O operations generated by the one or more applications whoseassociated datasets have been migrated to the second storage subsystem(412 b) begin to flow to the second storage subsystem (412 b).

As another example of automatically (904) performing corrective actions,the storage system (406) may limit the amount of resources that may beconsumed by particular applications. Consider an example in whichmultiple applications are being supported by a first storage subsystem(412 a) whose storage controllers are becoming constrained. In such anexample, the storage system (406) may be configured to automaticallythrottle one or more application isolation domains such that only apredetermined amount of IOPS, read bandwidth, write bandwidth, or someother quantifiable measure of total system resources for the storagesubsystem that supports the application isolation domain is madeavailable to the application isolation domain that is being throttled.

For further explanation, FIG. 10 sets forth a flow chart illustrating anexample method of providing application-specific storage by acloud-based storage system (1008 a, 1008 b, 1008 n) in accordance withsome embodiments of the present disclosure. Although depicted in lessdetail, the cloud-based storage systems (1008 a, 1008 b, 1008 n)depicted in FIG. 10 may be similar to the cloud-based storage systemsdescribed above, including those described with reference to FIG. 3C andFIG. 3D. In fact, the cloud-based storage systems (1008 a, 1008 b, 1008n) depicted in FIG. 10 may be embodied as one of the storage systemsdescribed above, as a combination of a plurality of the storage systemsdescribed above, as a modified version of one of the storage systemsdescribed above, as a combination of a plurality of modified versions ofthe storage systems described above, or any combination thereof that isexecuting within a cloud computing environment (1006).

The example method depicted in FIG. 10 also includes an application(1004) that is executing on a host (1002). The application (1004) may beembodied, for example, as one or more modules of computer programinstructions that are executing, directly or indirectly, on computerhardware such as a CPU. In fact, the application (1004) may be executingin a virtualized environment such as on one or more virtual machines,within one or more containers, or in some other way. In the examplemethod depicted in FIG. 10 , the application (1004) may be configured toutilized resources within the cloud-based storage system (1008 a, 1008b, 1008 n), for example, by issuing a request to read data from thecloud-based storage system (1008 a, 1008 b, 1008 n), by issuing arequest to write data to the cloud-based storage system (1008 a, 1008 b,1008 n), and so on. In the example method depicted in FIG. 10 , theapplication (1004) is executing on a host (1002). The host (1002) may beembodied, for example, as a server that is coupled for datacommunications with the cloud-based storage system (1008 a, 1008 b, 1008n) via one or more data communications networks and/or APIs into thecloud computing environment (1006), as a virtualized environment (e.g.,a virtual machine, a container) that is coupled for data communicationswith the cloud-based storage system (1008 a, 1008 b, 1008 n) via one ormore data communications networks and/or APIs into the cloud computingenvironment (1006). In alternative embodiments, the application (1004)may be executing elsewhere. For example, the application (1004) may beexecuting in the cloud computing environment (1006) itself.

The example method depicted in FIG. 10 includes identifying (1010), foran application (1004) that utilizes resources within the cloud-basedstorage system (1008 a, 1008 b, 1008 n), one or more characteristicsassociated with the application (1010). The one or more characteristicsassociated with the application (1004) may include information such as,for example, the amount of read bandwidth that needs to be provided tothe application (1004), the amount of write bandwidth that needs to beprovided to the application (1004), information describing service levelagreements that the application (1004) must adhere to, informationdescribing QoS requirements associated with the application (1004), andmany more.

In the example method depicted in FIG. 10 , the one or morecharacteristics associated with the application (1004) may be identified(1010) in a variety of ways. In one embodiment, for example, the one ormore characteristics associated with the application (1004) may beprovided to the cloud-based storage system (1008 a, 1008 b, 1008 n) (orto some management module that pairs applications with the appropriatecloud-based storage system) via a user interface. In such an example, asystem administrator or other authorized user may have access to aninterface that allows them to specify things like the amount of readbandwidth needed to support the application (1004), the number of IOPSthat need to be serviced by the application (1004), and so on. In thistype of embodiment, one or more characteristics associated with theapplication (1004) may be explicitly provided by a user.

In an alternative embodiment, the one or more characteristics associatedwith the application (1004) may be provided to the cloud-based storagesystem (1008 a, 1008 b, 1008 n) (or to some management module that pairsapplications with the appropriate cloud-based storage system) in a lessexplicit way. For example, a system administrator or other authorizeduser may have access to an interface that includes tunable elements(e.g., knobs) that allows them to specify things like a relative amountof performance that is needed by the application, a relative amount ofdata protection that is required by the application, a relative amountof data security that is required by the application, data compliancerequirements for data that is associated with the application, and soon. In such an example, each knob may be presented via a graphical userinterface and the user may set each knob to a value between 1-10, whereeach combination of settings maps to the underlying storage resourcesthat can provide the application with the best match to the combinationof settings. Similarly, users may be presented with various classes ofstorage to select from, where examples of such storage classes caninclude ‘low performance, low security’ storage, ‘high performance, lowsecurity’ storage, ‘low performance, high security’ storage, ‘highperformance, high security’ storage, and so on, including classes thathave fewer, additional, or other descriptors. In this type ofembodiment, one or more characteristics associated with the application(1004) may be inferred based on input provided by a user.

In an alternative embodiment, the one or more characteristics associatedwith the application (1004) may be provided to the cloud-based storagesystem (1008 a, 1008 b, 1008 n) (or to some management module that pairsapplications with the appropriate cloud-based storage system) not byuser input via a user interface, but rather through the use of APIs andsimilar tools. For example, an application developer may have access toan API that allows the software developer to specify things like thetype of application that the developer has developed, QoS aspectsrequested by the developer, security related aspects requested by thedeveloper, performance related aspects requested by the developer, andso on. In this type of embodiment, one or more characteristicsassociated with the application (1004) may be gathered by thecloud-based storage system (1008 a, 1008 b, 1008 n) (or to somemanagement module that pairs applications with the appropriatecloud-based storage system) by providing developers and systemadministrators with tools that enable the developers and systemadministrators to provide such information to the cloud-based storagesystem (1008 a, 1008 b, 1008 n) (or to some management module that pairsapplications with the appropriate cloud-based storage system).

In another alternative embodiment, the one or more characteristicsassociated with the application (1004) may be provided to thecloud-based storage system (1008 a, 1008 b, 1008 n) (or to somemanagement module that pairs applications with the appropriatecloud-based storage system) via information that describes theapplication (1004). The information that describes the application(1004) may be included in metadata that describes the application, theinformation that describes the application (1004) may be provided from auser such as the application developer, the information that describesthe application (1004) may be extracted from a service level agreementor similar description of the QoS required by the application (1004),and in other ways. In this type of embodiment, the one or morecharacteristics associated with the application (1004) may beextracted/extrapolated from the information that describes theapplication (1004).

In an alternative embodiment, the one or more characteristics associatedwith the application (1004) may be provided to the cloud-based storagesystem (1008 a, 1008 b, 1008 n) (or to some management module that pairsapplications with the appropriate cloud-based storage system) viamonitoring the execution of the application (1004). Monitoring theexecution of the application (1004) may be carried out, for example, bymonitoring the application (1004) to generate application levelstatistics for the application (1004) such as, for example, the amountof write bandwidth generated by the application, the amount of readbandwidth generated by the application, the number of IOPS generated bythe application, and so on. Likewise, the I/O patterns of theapplication (1004) may be monitored and compared to fingerprints forknown types of applications. For example, a database application maytend to generate I/O in a first pattern (e.g., random reads) whereas alog analysis application may tend to generate I/O in a second pattern(e.g., sequential reads). In such an example, when the I/O patterns of aparticular application (1004) match up reasonably well with fingerprintsfor known types of applications, the cloud-based storage system (1008 a,1008 b, 1008 n) may assume that the one or more characteristicsassociated with the application (1004) are similar to similarapplications. In this type of embodiment, the one or morecharacteristics associated with the application (1004) may be generatedby observing the actual execution of the application (1004).

Readers will appreciate that identifying (1010) one or morecharacteristics associated with the application (1004) may be carriedout in additional ways and that some characteristics associated with theapplication (1004) may be identified (1010) in one way while othercharacteristics associated with the application (1004) may be identified(1010) in another way. For example, some characteristics associated withthe application (1004) may be identified (1010) by observing the actualexecution of the application (1004) whereas other characteristicsassociated with the application (1004) may be explicitly provided by auser.

The example method depicted in FIG. 10 also includes selecting (1012),in dependence upon the one or more characteristics associated with theapplication (1004) and characteristics of resources (1016) within one ormore cloud-based storage system (1008 a, 1008 b, 1008 n), one or moreresources (1016) within the cloud-based storage system (1008 a, 1008 b,1008 n) to support the execution of the application (1004). Because thecloud-based storage system (1008 a, 1008 b, 1008 n) can include a largepool of disparate resources, the one or more characteristics associatedwith the application (1004) and the characteristics of each of theresources (1016) within the cloud-based storage system (1008 a, 1008 b,1008 n) can be used to select (1012) which particular resources withinan existing cloud-based storage system (1008 a, 1008 b, 1008 n) shouldbe used to provide storage to the application (1004). Assume, forexample, that a first storage system virtual controller compute instance(368 a) is embodied as a relatively powerful EC2 instance that canprocess a relatively large number of I/O operations in a relativelyshort period of time, whereas a second storage system virtual controllercompute instance (368 b) is embodied as a relatively lightweight EC2instance that can only process a relatively small number of I/Ooperations in the same period of time. In such an example, if anapplication (1004) has relatively high performance demands, the firststorage system virtual controller compute instance (368 a) may beselected (1012) to serve as the primary controller (as described above)for the application (1004), whereas if the application (1004) hasrelatively low performance demands, the second storage system virtualcontroller compute instance (368 b) may be selected (1012) to serve asthe primary controller (as described above) for the application (1004).As such, selecting (1012) one or more resources (1016) within the one ormore cloud-based storage systems (1008 a, 1008 b, 1008 n) to support theexecution of the application (1004) can include selecting, from theresources within a single cloud-based storage system, a subset of theresources within the cloud-based storage system to support theapplication (1004).

In some embodiments, because a new cloud-based storage system (1008 a,1008 b, 1008 n) can be quickly created with distinct pools of resources,the one or more characteristics associated with the application (1004)and the characteristics of resources (1016) that could be included in anewly created cloud-based storage system (1008 a, 1008 b, 1008 n) can beused to determine the particular configuration (including anidentification of the amount and types of resources) of a cloud-basedstorage system (1008 a, 1008 b, 1008 n) that should be created and usedto provide storage to the application (1004). Assume, for example, thatthe application (1004) has relatively high performance demands. In suchan example, a relatively powerful EC2 instance may be selected forinclusion in a cloud-based storage system (1008 a, 1008 b, 1008 n) thatis created for use by the application (1004). If the application (1004)has relatively low performance demands, however, a less powerful andrelatively lightweight EC2 instance may be selected for inclusion in acloud-based storage system (1008 a, 1008 b, 1008 n) that is created foruse by the application (1004). As such, selecting (1012) one or moreresources (1016) within the one or more cloud-based storage systems(1008 a, 1008 b, 1008 n) to support the execution of the application(1004) can include selecting, from available resources that could beincluded in a cloud-computing system, a subset of resources that can beprovided by the cloud computing environment (1006) for inclusion withina cloud-based storage system (1008 a, 1008 b, 1008 n) that is to becreated to support the execution of the application (1004).

In embodiments where multiple cloud-based storage systems (1008 a, 1008b, 1008 n) are supported by the cloud computing environment (1006), theone or more characteristics associated with the application (1004) andthe characteristics the resources (1016) within each of the cloud-basedstorage systems (1008 a, 1008 b, 1008 n) can be used to select (1012)which particular cloud-based storage system (1008 a, 1008 b, 1008 n)should be used to provide storage to the application (1004). Assume, forexample, that a first cloud-based storage system (1008 a) is comprisedof resources that allow the first cloud-based storage system (1008 a) toprovide for relatively low latency, high bandwidth I/O operationswhereas a second cloud-based storage system (1008 b) is comprised ofresources that allow the second cloud-based storage system (1008 b) toprovide for relatively high latency, low bandwidth I/O operations. Insuch an example, if the one or more characteristics associated with theapplication (1004) indicated that the application (1004) had relativelyhigh performance demands, the first cloud-based storage system (1008 a)may be selected to support the execution of the application (1004). Ifthe one or more characteristics associated with the application (1004)indicated that the application (1004) had relatively low performancedemands, however, the second cloud-based storage system (1008 b) may beselected to support the execution of the application (1004). As such,selecting (1012) one or more resources (1016) within the one or morecloud-based storage systems (1008 a, 1008 b, 1008 n) to support theexecution of the application (1004) can include selecting, from amongsta plurality of cloud-based storage systems (1008 a, 1008 b, 1008 n) thatare supported by the cloud computing environment (1006), a particularcloud-based storage system (1008 a, 1008 b, 1008 n) to support theexecution of the application (1004).

Readers will appreciate that although the preceding paragraphs describeselecting (1012) one or more resources (1016) within the one or morecloud-based storage systems (1008 a, 1008 b, 1008 n) to support theexecution of the application (1004) by: 1) selecting, from the resourceswithin a single cloud-based storage system, a subset of the resourceswithin the single cloud-based storage system to support the execution ofthe application (1004), or 2) selecting, from available resources thatcould be included in a cloud-computing system, a subset of resourcesthat can be provided by the cloud computing environment (1006) forinclusion within a cloud-based storage system (1008 a, 1008 b, 1008 n)that is to be created to support the execution of the application(1004), or 3) selecting, from amongst a plurality of cloud-based storagesystems (1008 a, 1008 b, 1008 n) that are supported by the cloudcomputing environment (1006), a particular cloud-based storage system(1008 a, 1008 b, 1008 n) to support the execution of the application(1004), other methods of selecting (1012) one or more resources (1016)within the one or more cloud-based storage systems (1008 a, 1008 b, 1008n) to support the execution of the application (1004) are possible.

Readers will appreciate that combinations of the various methodsdescribed above for selecting (1012) one or more resources (1016) withinthe one or more cloud-based storage systems (1008 a, 1008 b, 1008 n) tosupport the execution of the application (1004) are also within thescope of the present disclosure. For example, selecting (1012) one ormore resources (1016) within the one or more cloud-based storage systems(1008 a, 1008 b, 1008 n) to support the execution of the application(1004) can be carried out by: 1) selecting, from amongst a plurality ofcloud-based storage systems (1008 a, 1008 b, 1008 n) that are supportedby the cloud computing environment (1006), a particular cloud-basedstorage system (1008 a, 1008 b, 1008 n) to support the execution of theapplication (1004), and 2) subsequently selecting, from the resourceswithin the single cloud-based storage system that was selected in step1, a subset of the resources within the single cloud-based storagesystem to support the execution of the application (1004). Readers willfurther appreciate that although, for ease of explanation, the examplesdescribed above relate to embodiments where applications are largelydescribed as being high performance or low performance, and cloud-basedstorage systems are largely described as being high performance or lowperformance, many other characteristics of the applications and thecloud-based storage systems may be taken into consideration.

In the example method depicted in FIG. 10 , at least a portion of adataset that is associated with the application (1004) is stored asblocks within block storage resources in the cloud-based storage system(1008 a, 1008 b, 1008 n) and also stored as objects within objectstorage resources in the cloud-based storage system (1008 a, 1008 b,1008 n). The dataset that is associated with the application (1004) isdepicted in FIG. 10 as a series of dataset slices (1014 a, 1014 b, 1014n) that collectively form the dataset that is associated with theapplication (1004). Such a dataset may be ‘associated’ with theapplication (1004) as the application writes data to the dataset, readsdata from the dataset, and so on. In the example depicted in FIG. 10 ,each dataset slice (1014 a, 1014 b, 1014 n) is stored as blocks withinblock storage resources in the cloud-based storage system (1008 a, 1008b, 1008 n). More specifically, each dataset slice (1014 a, 1014 b, 1014n) is stored as blocks of data that are contained within one or moreVDrives (372 a, 372 b, 372 n), which are also referred to as virtualdrives and cloud computing instances with local storage herein. In theexample depicted in FIG. 10 , each dataset slice (1014 a, 1014 b, 1014n) is also stored as objects within object storage resources in thecloud-based storage system (1008 a, 1008 b, 1008 n). More specifically,each dataset slice (1014 a, 1014 b, 1014 n) is stored as one or moreobjects within one or more S3 buckets (392), which are also referred toas cloud-based object storage herein. As described in more detail above,in some embodiments the entire dataset may reside within the blockstorage layer of the cloud-based storage system (1008 a, 1008 b, 1008 n)while in some embodiments only some portion of the entire dataset mayreside within the block storage layer of the cloud-based storage system(1008 a, 1008 b, 1008 n).

For further explanation, FIG. 11 sets forth a flow chart illustrating anexample method of providing application-specific storage by acloud-based storage system (1008 a, 1008 b, 1008 n) in accordance withsome embodiments of the present disclosure. The example method depictedin FIG. 11 is similar to the example method depicted in FIG. 10 , as theexample method depicted in FIG. 11 also includes identifying (1010), foran application (1004) that utilizes resources within the cloud-basedstorage system (1008 a, 1008 b, 1008 n), one or more characteristicsassociated with the application (1010) and selecting (1012), independence upon the one or more characteristics associated with theapplication (1004) and characteristics of resources (1016) within one ormore cloud-based storage system (1008 a, 1008 b, 1008 n), one or moreresources (1016) within the cloud-based storage system (1008 a, 1008 b,1008 n) to support the execution of the application (1004). Although notexplicitly depicted in FIG. 11 , at least a portion of a datasetassociated with the application is stored as blocks within block storageresources in the cloud-based storage system (1008 a, 1008 b, 1008 n) andalso stored as objects within object storage resources in thecloud-based storage system (1008 a, 1008 b, 1008 n).

In the example method depicted in FIG. 11 , selecting (1012) one or moreresources (1016) within the cloud-based storage system (1008 a, 1008 b,1008 n) to support the execution of the application (1004) can includeselecting (1102), from amongst a plurality of storage system virtualcontroller compute instances, a particular storage system virtualcontroller compute instances to service I/O operations generated by theapplication (1004). As described above, in embodiments where only asingle storage system virtual controller compute instance (368 a) canserve as the active (i.e. primary) controller, the storage systemvirtual controller compute instance that best matches the needs of theapplication (1004) as indicated in the characteristics of theapplication (1004), may be selected (1102). Readers will appreciate thatin some embodiments compute instances can be created on demand and don'tactually need to be selected from a set of existing compute instances.In fact, it is plausible that the virtual storage system controllerlogic could be embodied as a Lambda function or similar construct,meaning it is run on demand on one or more compute hosts in response toincrease and decrease in demand (subject to pricing constraints whichcould be related to the amount of money a customer is willing to pay fora particular application). In such an example, virtual storage systemcontroller compute instances may be instantiated according to acombination of the application's current run-time characteristics, itsSLA, the customer's budget constraints, and so on.

In the example method depicted in FIG. 11 , selecting (1012) one or moreresources (1016) within the cloud-based storage system (1008 a, 1008 b,1008 n) to support the execution of the application (1004) can includeselecting (1104), from available resources that could be included in acloud-based storage system, a subset of resources that can be providedby the cloud computing environment (1006) for inclusion within thecloud-based storage system that is to be created to support theexecution of the application (1004). Selecting (1104) a subset ofresources that can be provided by the cloud computing environment (1006)for inclusion within the cloud-based storage system that is to becreated to support the execution of the application (1004) may becarried out, for example, through the use of one or more formulas/rulesthat may be used to dynamically determine the type and amount of variouscomponents that should be included within the cloud-based storage systemthat is to be created to support the execution of the application(1004). If characteristics associated with a particular application(1004) indicate that the application (1004) will issue a certain numberof IOPS during peak usage, for example, an EC2 instance that can executethe storage controller application in such a way that the storagecontroller application can service that number of IOPS should beselected (1104) for inclusion within the cloud-based storage system thatis to be created to support the execution of the application (1004).Likewise, a sufficient number and type of EC2 instances that are used asvirtual drives should be selected (1104) for inclusion within thecloud-based storage system that is to be created to support theexecution of the application (1004) such that the virtual drives canservice the number of IOPS that the application (1004) is expected togenerate.

In an alternative embodiment, one or more templates may also be utilizedto select (1104) a subset of resources that can be provided by the cloudcomputing environment (1006) for inclusion within the cloud-basedstorage system that is to be created to support the execution of theapplication (1004). Such templates may include, for example, anidentification of a set of resources that are to be included in acloud-based storage system that is to be used to support applications ofa particular type (e.g., a first template may be used for databaseapplications whereas a second template may be used for emailapplications), an identification of a set of resources that are to beincluded in a cloud-based storage system that is to be used to supportapplications of that match a particular profile based on thecharacteristics of the application, and so on. Readers will appreciatethat once a subset of resources that can be provided by the cloudcomputing environment (1006) for inclusion within the cloud-basedstorage system that is to be created to support the execution of theapplication (1004) have been selected (1104), an instance of such acloud-based storage system may be created to support the execution ofthe application (1004) by provisioning such resources from the cloudcomputing environment and configuring such resources to operate as acloud-based storage system (e.g., configuring the EC2 instances thathost the storage controller application to communicate with the EC2instances that are to be used as virtual drives, configuring the EC2instances that are to be used as virtual drives to communicate with S3buckets, and so on).

In the example method depicted in FIG. 11 , selecting (1012) one or moreresources (1016) within the cloud-based storage system (1008 a, 1008 b,1008 n) to support the execution of the application (1004) can includeselecting (1106), from amongst a plurality of cloud-based storagesystems (1008 a, 1008 b, 1008 n) that are supported by the cloudcomputing environment (1006), a particular cloud-based storage system tosupport the execution of the application (1004). Selecting (1106), fromamongst a plurality of cloud-based storage systems (1008 a, 1008 b, 1008n) that are supported by the cloud computing environment (1006), aparticular cloud-based storage system to support the execution of theapplication (1004) may be carried out, for example, by comparing thecharacteristics associated with the application (1004) to thecharacteristics associated with each of the cloud-based storage systems(1008 a, 1008 b, 1008 n) to identify a best fit (i.e. to identify acloud-based storage system (1008 a, 1008 b, 1008 n) that best providesthe resources needed by the application (1004)).

In the example method depicted in FIG. 11 , selecting (1012) one or moreresources (1016) within the cloud-based storage system (1008 a, 1008 b,1008 n) to support the execution of the application (1004) can includeselecting, from resources within a single cloud-based storage system(1008 a), a subset of the resources within the single cloud-basedstorage system (1008 a) to support the execution of the application(1004). Selecting a subset of the resources within the singlecloud-based storage system to support the execution of the application(1004) may be carried out, for example, by selecting a particularresource within the cloud-based storage system (1008 a) that is bestsuited to meet the needs of the application. For example, and asdescribed above, when only a single storage system virtual controllercompute instance (368 a) can serve as the active (i.e. primary)controller, the storage system virtual controller compute instance thatbest matches the needs of the application (1004) as indicated in thecharacteristics of the application (1004), may be selected.Alternatively, selecting a subset of the resources within the singlecloud-based storage system to support the execution of the application(1004) may be carried out by selecting a particular group of resourceswithin the cloud-based storage system (1008 a) that are best suited tomeet the needs of the application (1004). For example, if thecharacteristics associated with the application (1004) indicate that theapplication (1004) requires a relatively low write latency andrelatively high bandwidth, the subset of the resources within the singlecloud-based storage system (1008 a) that may be selected (1102) tosupport the execution of the application (1004) can include the virtualdrives within the cloud-based storage system that are supported byrelatively powerful EC2 instances. Alternatively, if the characteristicsassociated with the application (1004) indicate that the application(1004) has flexibility with respect to the write latency and throughput,the subset of the resources within the single cloud-based storage system(1008 a) that may be selected (1102) to support the execution of theapplication (1004) can include the virtual drives within the cloud-basedstorage system that are supported by less powerful EC2 instances.

For further explanation, FIG. 12 sets forth a flow chart illustrating anexample method of providing application-specific storage by acloud-based storage system (1008 a, 1008 b, 1008 n) in accordance withsome embodiments of the present disclosure. The example method depictedin FIG. 12 is similar to the example methods depicted in FIG. 10 andFIG. 11 , as the example method depicted in FIG. 12 also includesidentifying (1010), for an application (1004) that utilizes resourceswithin the cloud-based storage system (1008 a, 1008 b, 1008 n), one ormore characteristics associated with the application (1010) andselecting (1012), in dependence upon the one or more characteristicsassociated with the application (1004) and characteristics of resources(1016) within one or more cloud-based storage system (1008 a, 1008 b,1008 n), one or more resources (1016) within the cloud-based storagesystem (1008 a, 1008 b, 1008 n) to support the execution of theapplication (1004). Although not explicitly depicted in FIG. 12 , atleast a portion of a dataset associated with the application is storedas blocks within block storage resources in the cloud-based storagesystem (1008 a, 1008 b, 1008 n) and also stored as objects within objectstorage resources in the cloud-based storage system (1008 a, 1008 b,1008 n).

The example method depicted in FIG. 12 also includes predicting (1202) achange to the application (1004). Predicting (1202) a change to theapplication (1004) may be carried out, for example, by performing atime-series analysis of various performance metrics associated with theapplication (1004) to identify trends associated with the application(1004), by examining telemetry data collected for similar applicationsto identify trends associated with the similar applications, or in someother way. As such, an examination of such information can be used togenerate trending information for the applications (1004) including, forexample, information describing the rate at which the number of IOPSbeing generated by the application (1004) has been changing, the rate atwhich overwrite rates for I/O operations that are being generated by theapplication (1004) are changing, the rate at which the amount of readbandwidth that is being consumed by I/O operations generated by theapplication (1004) is changing, and many others. In such a way,predicted characteristics of the application (1004) may be generated byextrapolating identified trends out over a period of time in the future.

Consider an example in which telemetry data gathered from a plurality ofstorage systems indicates that, on average, the amount of CPU resourcesrequired to support a virtual desktop infrastructure workload doublesevery three years. In such an example, if a particular application(1004) is a virtual desktop infrastructure workload, predicting (1202) achange to the application (1004) may be carried out, at least in part,by determining the amount of CPU resources currently required to supportthe application (1004) and assuming that the amount of CPU resourcesthat will be required to support the application (1004) in the futurewill double every three years. In such a way, the load demands createdby each application (1004) may be projected to some point in the future.

The example method depicted in FIG. 12 also includes identifying (1206),in dependence upon the predicted change to the application (1004), oneor more updated characteristics associated with the application (1004).Identifying (1206) one or more updated characteristics associated withthe application (1004) may be carried out, for example, by utilizingtrending information associated with the application (1004) itself,trending information associated with the application (1004) itself andtrending information based on telemetry data associated with similarapplications, or some combination thereof, to project how theapplication (1004) will operate in the future and to project whatcharacteristics the application (1004) will exhibit in the future.Readers will appreciate that embodiments described herein may alsoleverage machine learning techniques to not only predicting (1202) achange to the application (1004) but also to identify (1206) one or moreupdated characteristics associated with the application (1004). In suchan example, various metrics associated with the application (1004) andeven similar applications may be fed into a machine learning model todetect the occurrence of changes to the application (1004), changes tothe application's (1004) usage of underlying storage resources, and soon, to gain a better understanding of characteristics that the workloadmay take on in the future.

The example method depicted in FIG. 12 also includes detecting (1204) achange to the application (1004). Detecting (1204) a change to theapplication (1004) may be carried out, for example, by detecting achange to some configuration parameter associated with the application(1004), by detecting a change to the usage of the application (1004)(e.g., the number of users of a particular application has changed by apredetermined amount), by detecting that the application (1004) hasbegun exhibiting behavior that is outside of expected boundaries (e.g.,the application has begun generating an amount of IOPS that are outsideof an expected range), and in other ways. In response to detecting(1204) a change to the application (1004), the characteristics of theapplication (1004) may be reevaluated.

The example method depicted in FIG. 12 also includes identifying (1208),in dependence upon the detected change to the application (1004), one ormore updated characteristics associated with the application (1004).Identifying (1208) one or more updated characteristics associated withthe application (1004) may be carried out, for example, by performingthe same analysis and actions described above with reference to at leaststep 1010 of FIG. 10 , using the updated state of the application (1004)as input to such analysis. In such a way, the detection (1204) of achange to the application (1004) can essentially cause a managementmodule or other appropriate actor to re-perform the analysis and actionsto identify (1208) one or more updated characteristics associated withthe application (1004).

The example method depicted in FIG. 12 also includes selecting (1210),in dependence upon the one or more updated characteristics associatedwith the application (1004) and characteristics of one or more resourceswithin one or more of the cloud-based storage systems (1008 a, 1008 b,1008 n), an updated set of resources within the one or more cloud-basedstorage systems (1008 a, 1008 b, 1008 n) to support the execution of theapplication (1004). Selecting (1210) an updated set of resources withinthe one or more cloud-based storage systems (1008 a, 1008 b, 1008 n) tosupport the execution of the application (1004) may be carried out, forexample, by performing the same analysis and actions described abovewith reference to at least step 1012 of FIG. 10 , using the one or moreupdated characteristics associated with the application (1004). In sucha way, the identification (1208) of one or more updated characteristicsassociated with the application (1004) can essentially cause amanagement module or other appropriate actor to re-perform the analysisand actions to select (1210) an updated set of resources within the oneor more cloud-based storage systems (1008 a, 1008 b, 1008 n) to supportthe execution of the application (1004).

In the example method depicted in FIG. 12 , selecting (1210) an updatedset of resources within the one or more cloud-based storage systems(1008 a, 1008 b, 1008 n) to support the execution of the application(1004) can include modifying (1212) resources within the one or morecloud-based storage systems (1008 a, 1008 b, 1008 n). Modifying (1212)resources within the one or more cloud-based storage systems (1008 a,1008 b, 1008 n) may be carried out, for example, by creating newresources that are to be added to the one or more cloud-based storagesystems (1008 a, 1008 b, 1008 n). Consider an example in which anevaluation of the updated characteristics associated with theapplication (1004) indicated that an updated set of resources within theone or more cloud-based storage systems (1008 a, 1008 b, 1008 n) thatare needed to support the execution of the application (1004) wouldinclude additional capacity beyond the capacity that is offered by aparticular cloud-based storage system (1008 a) that is currentlysupporting the execution of the application (1004). In such an example,modifying (1212) resources within the one or more cloud-based storagesystems (1008 a, 1008 b, 1008 n) may be carried out by adding additionalvirtual drives to the particular cloud-based storage system (1008 a)that is currently supporting the execution of the application (1004), bycreating additional EC2 instances with local storage and adding thenewly created EC2 instances with local storage to the pool of resourcesthat are utilized by the storage system virtual controller computeinstances.

In some embodiments, modifying (1212) resources within the one or morecloud-based storage systems (1008 a, 1008 b, 1008 n) may be carried out,for example, by creating new resources that are to be used asreplacements for existing resources within one or more of thecloud-based storage systems (1008 a, 1008 b, 1008 n). Consider anexample in which an evaluation of the updated characteristics associatedwith the application (1004) indicated that an updated set of resourceswithin the one or more cloud-based storage systems (1008 a, 1008 b, 1008n) that are needed to support the execution of the application (1004)would include a more powerful cloud-computing instance that is needed tosupport the execution of the storage controller application than iscurrently offered by a particular cloud-based storage system (1008 a)that is supporting the execution of the application (1004). In such anexample, modifying (1212) resources within the one or more cloud-basedstorage systems (1008 a, 1008 b, 1008 n) may be carried out by creatinga new, more powerful EC2 instance that is executing the storagecontroller application, configuring the new EC2 instance to utilize thestorage resources that are included in the particular cloud-basedstorage system (1008 a) that is supporting the execution of theapplication (1004), and using the new EC2 instance to service I/Ooperations that are directed to the particular cloud-based storagesystem (1008 a) by the application (1004), while also terminating (ormoving into a standby role) the EC2 instance that was previouslyservicing I/O operations that are directed to the particular cloud-basedstorage system (1008 a) by the application (1004). In such a way,modifying (1212) resources within the one or more cloud-based storagesystems (1008 a, 1008 b, 1008 n) can include modifying (1214) a storagecontroller layer in at least one of the cloud-based storage systems(1008 a, 1008 b, 1008 n), where the storage controller layer includeseach of the cloud-computing instances that are used to support theexecution of the storage controller application. Readers will appreciatethat modifying (1214) a storage controller layer in at least one of thecloud-based storage systems (1008 a, 1008 b, 1008 n) can includecreating additional cloud-computing instances that are used to supportthe execution of the storage controller application, terminatingcloud-computing instances that are used to support the execution of thestorage controller application, replacing a first cloud-computinginstances that is used to support the execution of the storagecontroller application with a second cloud-computing instances that isused to support the execution of the storage controller application, andso on.

Readers will appreciate that although modifying (1214) a storagecontroller layer in at least one of the cloud-based storage systems(1008 a, 1008 b, 1008 n) can be carried out by expanding or contractingthe number of virtual storage system controller instances, modifying(1214) a storage controller layer in at least one of the cloud-basedstorage systems (1008 a, 1008 b, 1008 n) can be carried out in otherways. For example, modifying (1214) a storage controller layer in atleast one of the cloud-based storage systems (1008 a, 1008 b, 1008 n)may be carried by upgrading or downgrading the performance and othercharacteristics of a particular virtual storage system controllerinstance, by replacing one or more virtual storage system controllercompute instances with one or more virtual storage system controllercompute instances with different performance and other characteristics,and in other ways.

Modifying (1212) resources within the one or more cloud-based storagesystems (1008 a, 1008 b, 1008 n) can also include modifying (1216) avirtual drive layer in at least one of the cloud-based storage systems(1008 a, 1008 b, 1008 n), where the virtual drive layer includes thevirtual drives in a particular cloud-based storage system (1008 a, 1008b, 1008 n). Modifying (1216) a virtual drive layer in at least one ofthe cloud-based storage systems (1008 a, 1008 b, 1008 n), where thevirtual drive layer includes the virtual drives in a particularcloud-based storage system (1008 a, 1008 b, 1008 n) may be carried out,for example, by creating additional cloud computing instances with localstorage to be used as virtual drives, by terminating cloud computinginstances with local storage that were previously used as virtualdrives, by replacing a first cloud computing instance with local storagethat was used as a virtual drive with a second cloud computing instancewith local storage that is to be used as a virtual drive, and so on. Insuch an example, it ultimately may be necessary to migrate data on afirst virtual drive to a second virtual to more evenly distribute theportion of the dataset that is to be stored as blocks in block storage(thereby presumably more evenly distributing reads of the dataset).

Consider an example in which modifying (1216) a virtual drive layer inat least one of the cloud-based storage systems (1008 a, 1008 b, 1008 n)includes adding X number of new virtual drives to a particularcloud-based storage system (1008 a). In such an example, the writebandwidth for the cloud-based storage system (1008 a) immediatelyincreases as the X number of new virtual drives are available to servicewrite operations. Without migrating the portions of the dataset that arestored in the already existing virtual drives, however, read bandwidthis not impacted much as only the already existing virtual drives areavailable for servicing read operations given that they are the onlydrives that include the dataset. By migrating at least some portions ofthe dataset that was stored on the already existing virtual drives tothe X number of new virtual drives, both the X number of new virtualdrives and the already existing drives may be available for servicingread operations as all of the virtual drives include at least someportion of the dataset. Readers will appreciate that some portion of thedataset that is stored on the already existing virtual drives to the Xnumber of virtual drives may be migrated immediately upon the creationof the X number of new virtual drives, as resources are available tomigrate data, according to some predetermined schedule, or in some otherway (including not migrating data at all). Readers will appreciate thatit also may be necessary to migrate data in response to other types ofchanges to the virtual drive layer.

Readers will appreciate that although modifying (1216) a virtual drivelayer in at least one of the cloud-based storage systems (1008 a, 1008b, 1008 n) may be carried out to expand or contract the number of suchvirtual drive instances, thereby allowing an immediate change in writebandwidth, and allowing an eventual change in read bandwidth dependenton migrating data to rebalance the content, modifying (1216) a virtualdrive layer in at least one of the cloud-based storage systems (1008 a,1008 b, 1008 n) could be carried out in other ways. For example,modifying (1216) a virtual drive layer in at least one of thecloud-based storage systems (1008 a, 1008 b, 1008 n) could be carriedout by changing the configured performance of one or more virtualdrives, changing networking resources that are used by the virtualdrives and to access the virtual drives, by changing storagecharacteristics of individual virtual drive instances, and in otherways.

In the example method depicted in FIG. 12 , after selecting (1210) anupdated set of resources within the one or more cloud-based storagesystems (1008 a, 1008 b, 1008 n) to support the execution of theapplication (1004) (including any associated modifications), the updatedset of resources within the one or more cloud-based storage systems(1008 a, 1008 b, 1008 n) are ultimately used to support the execution ofthe application (1004). In these embodiments, however, the application(1004) may utilize the updated set of resources within the one or morecloud-based storage systems (1008 a, 1008 b, 1008 n) without migratingany portion of the dataset that is stored as objects within objectstorage resources in the cloud-based storage system (1008 a, 1008 b,1008 n). As will be described in greater detail below, when additionalvirtual drives are added some pieces of the dataset that are stored asblocks in the block storage virtual drives may need to be migratedbetween virtual drives such that the dataset is distributed relativelyevenly across the virtual drives. Such migration is not needed, however,for the portions of the dataset that are already stored as objectswithin object storage, as the object storage effectively has unlimitedcapacity, especially where the object storage is S3, and relativelysteady read latencies regardless of where the data is actually located.

For further explanation, FIG. 13 sets forth a flow chart illustrating anexample method of providing application-specific storage by acloud-based storage system (1008 a, 1008 b, 1008 n) in accordance withsome embodiments of the present disclosure. The example method depictedin FIG. 13 is similar to the example methods depicted in FIGS. 10-12 ,as the example method depicted in FIG. 13 also includes identifying(1010), for an application (1004) that utilizes resources within thecloud-based storage system (1008 a, 1008 b, 1008 n), one or morecharacteristics associated with the application (1010) and selecting(1012), in dependence upon the one or more characteristics associatedwith the application (1004) and characteristics of resources (1016)within one or more cloud-based storage system (1008 a, 1008 b, 1008 n),one or more resources (1016) within the cloud-based storage system (1008a, 1008 b, 1008 n) to support the execution of the application (1004).Although not explicitly depicted in FIG. 13 , at least a portion of adataset associated with the application is stored as blocks within blockstorage resources in the cloud-based storage system (1008 a, 1008 b,1008 n) and also stored as objects within object storage resources inthe cloud-based storage system (1008 a, 1008 b, 1008 n).

The example method depicted in FIG. 13 also includes detecting (1302) achange to one or more of the cloud-based storage systems (1008 a, 1008b, 1008 n). Detecting (1302) a change to one or more of the cloud-basedstorage systems (1008 a, 1008 b, 1008 n) may be carried out, forexample, by detecting that one or more resources (e.g., one or morevirtual drives, one or more storage system virtual controller computeinstances) have failed or otherwise become unavailable, by detectingthat one or more resources within a cloud-based storage system (1008 a,1008 b, 1008 n) has become more/less available due to a change in usageof the cloud-based storage systems (1008 a, 1008 b, 1008 n), bydetecting a change to the configuration of a cloud-based storage system(1008 a, 1008 b, 1008 n), or in some other way.

The example method depicted in FIG. 13 also includes identifying (1304),in dependence upon the detected change to one or more of the cloud-basedstorage systems (1008 a, 1008 b, 1008 n), one or more updatedcharacteristics associated with the one or more cloud-based storagesystems (1008 a, 1008 b, 1008 n). Identifying (1304) one or more updatedcharacteristics associated with the one or more cloud-based storagesystems (1008 a, 1008 b, 1008 n) may be carried out, for example, bymonitoring the performance of the cloud-based storage systems (1008 a,1008 b, 1008 n), by extrapolating the one or more updatedcharacteristics associated with the one or more cloud-based storagesystems (1008 a, 1008 b, 1008 n) based on characteristics associatedwith the component parts of the cloud-based storage systems (1008 a,1008 b, 1008 n), or in other ways. In such a way, the detection (1302)of a change to one or more of the cloud-based storage systems (1008 a,1008 b, 1008 n) can essentially cause a management module or otherappropriate actor to re-perform the analysis and actions to identify(1304) one or more updated characteristics associated with the one ormore cloud-based storage systems (1008 a, 1008 b, 1008 n).

The example method depicted in FIG. 13 also includes selecting (1306),in dependence upon the characteristics associated with the application(1004) and the updated characteristics associated with the one or morecloud-based storage systems (1008 a, 1008 b, 1008 n), an updated set ofresources within the one or more cloud-based storage systems (1008 a,1008 b, 1008 n) to support the execution of the application (1004).Selecting (804) one or more storage resources within the storage system(406) to support the execution of the application (404) may be carriedout, for example, by comparing the one or more characteristicsassociated with the application (404) and the updated characteristics ofstorage resources within the storage system (406) to identify a bestfit. In such an example, multiple aspects of the one or morecharacteristics associated with the application (404) and the updatedcharacteristics of storage resources within the storage system (406) maybe taken into consideration, in a weighted or unweighted fashion, toidentify the set of storage resources that would provide the best fitfor the application (404) given the updated characteristics of resourceswithin the storage system (406).

For further explanation, FIG. 14 sets forth a flow chart illustrating anexample method of providing application-specific storage by acloud-based storage system (1008 a, 1008 b, 1008 n) in accordance withsome embodiments of the present disclosure. The example method depictedin FIG. 14 is similar to the example methods depicted in FIGS. 10-13 ,as the example method depicted in FIG. 14 also includes identifying(1010), for an application (1004) that utilizes resources within thecloud-based storage system (1008 a, 1008 b, 1008 n), one or morecharacteristics associated with the application (1010) and selecting(1012), in dependence upon the one or more characteristics associatedwith the application (1004) and characteristics of resources (1016)within one or more cloud-based storage system (1008 a, 1008 b, 1008 n),one or more resources (1016) within the cloud-based storage system (1008a, 1008 b, 1008 n) to support the execution of the application (1004).Although not explicitly depicted in FIG. 14 , at least a portion of adataset associated with the application is stored as blocks within blockstorage resources in the cloud-based storage system (1008 a, 1008 b,1008 n) and also stored as objects within object storage resources inthe cloud-based storage system (1008 a, 1008 b, 1008 n).

The example method depicted in FIG. 14 includes detecting (1402) thatone or more resources (1016 a, 1016 b, 1016 n) within one or more of thecloud-based storage systems (1008 a, 1008 b, 1008 n) have becomeconstrained. Detecting (1402) that one or more resources (1016 a, 1016b, 1016 n) within one or more of the cloud-based storage systems (1008a, 1008 b, 1008 n) have become constrained may be carried out, forexample, by detecting that the utilization of a particular resourceeither has reached a predetermined utilization level (e.g., 90% of fullutilization). Consider an example in which multiple applications aresupported by the same resources (e.g., the same virtual controllers, thesame virtual drives). In such an example, the extent to which oneapplication consumes resources can impact the extent to which anotherapplication has access to system resources. For example, if a firstapplication begins generating very large amount of I/O operations, asecond application may experience a delay in having its I/O operationsserviced as each application may route its I/O operations through thesame set of virtual controllers. In such an example, detecting (1402)that one or more resources (1016 a, 1016 b, 1016 n) within one or moreof the cloud-based storage systems (1008 a, 1008 b, 1008 n) have becomeconstrained may be carried out, for example, by detecting that theutilization of the virtual controllers has reached the predeterminedutilization level.

The example method depicted in FIG. 14 also includes automatically(1404), without user intervention, performing corrective actions. In theexample method depicted in FIG. 14 , the corrective actions may take avariety of forms. For example, automatically (1404) performingcorrective actions may include, adding additional resources to aparticular cloud-based storage system (1008 a, 1008 b, 1008 n),replacing less powerful resources in the cloud-based storage system(1008 a, 1008 b, 1008 n) with more powerful resources, and so on.Consider an example in which the virtual controllers for a particularcloud-based storage system (1008 a) are becoming constrained. In such anexample, automatically (1404) performing corrective actions may becarried out, for example, by creating additional virtual controllers andconfiguring one or more applications that were utilizing theover-constrained virtual controller to use the newly created virtualcontroller. Readers will appreciate that other modifications to one ormore of the cloud-based storage systems (1008 a, 1008 b, 1008 n)described above may also be performed automatically as part ofautomatically (1404) performing corrective actions.

As another example of automatically (1404) performing correctiveactions, applications may be reconfigured to utilize different, lessutilized resources within one or more of the cloud-based storage systems(1008 a, 1008 b, 1008 n). Readers will appreciate that, as describedabove, such a reconfiguration may occur without migrating data that isalready stored as objects within object storage resources. Readers willfurther appreciate that although FIG. 14 relates to an embodiment thatdetects (1402) that one or more resources (1016 a, 1016 b, 1016 n)within one or more of the cloud-based storage systems (1008 a, 1008 b,1008 n) have become constrained, in some embodiment, such a detectionmay be preemptive such that corrective actions are taken in response todetecting (1402) that one or more resources (1016 a, 1016 b, 1016 n)within one or more of the cloud-based storage systems (1008 a, 1008 b,1008 n) will become constrained.

The example method depicted in FIG. 14 also includes receiving (1406),via a storage tuning interface (an example of which is depicted in FIG.15 and described below), tuning information for a cloud-based storagesystem (1008 a, 1008 b, 1008 n) that is utilized by the application(1404). Such tuning information can include information describing, forexample, the amount of storage performance a user would like to beavailable to the application (1404). The storage performance mayinclude, for example, the amount of IOPS that the storage should be ableto service, the read latency that the storage should be able to provide,the write latency that the storage should be able to provide, and so on.Likewise, tuning information can include information describing theamount and type of capacity that should be provided to the application(1404). In addition, the tuning information can include other types oftuning information as described below with reference to FIG. 15 . Inresponse to receiving (1406) tuning information, resources within one ormore of the cloud-based storage systems (1008 a, 1008 b, 1008 n) may bemodified (1212 in FIG. 12 ) as described above.

For further explanation, FIG. 15 sets forth a graphical user interface(‘GUI’) (1502) for tuning storage according to some embodiments of thepresent disclosure. The GUI (1502) depicted in FIG. 15 includes tunableknobs (1504, 1506, 1508, 1510) that enable a user of the GUI (1502) totune the storage that is provided a particular application. In theexample GUI (1502) depicted in FIG. 15 , a user of the GUI (1502) maytune storage for a particular application that is selected from anapplication selection dropdown menu (1512), although in otherembodiments the GUI (1502) could utilize other mechanisms for selectinga particular application, including selecting the particular applicationprior to launching an instance of the GUI. In such a way, storage may betuned on a per-application basis, meaning that storage may be tuned inone way for a first application and tuned in another way for secondapplication.

The example GUI (1502) depicted in FIG. 15 includes four knobs:

-   -   a. A performance knob (1504) that enables a user to select the        amount of storage performance that the user would like to be        available to the application. The storage performance may        include, for example, the amount of IOPS that the storage should        be able to service, the read latency that the storage should be        able to provide, the write latency that the storage should be        able to provide, and so on. In such an example, the user may be        able to view the various performance metrics that can be        delivered at each value on the performance knob (1504) in a        variety of ways. For example, hovering a mouse pointer over a        particular value (e.g., ‘6’) on the service knob (1504) may        cause a pop-up to be generated that identifies the number of        IOPS that the storage will perform at that value on the service        knob (1504), a read latency that the storage will deliver at        that value on the service knob (1504), and so on. Alternatively,        a second window may exist to display the various performance        metrics that can be delivered at the currently selected value on        the performance knob (1504). Readers will appreciate that a        secondary window, pop-up, or other medium for conveying        information to the user can include additional information,        including the costs associated with a particular value on the        performance knob (1504).    -   b. A recovery knob (1506) that enables a user to select how        available the storage is to a particular application. In such an        example, a particular recovery value on the recovery knob (1506)        may be associated with a particular availability guarantee        (e.g., the storage is guaranteed to be available 99.9999% of the        time), a particular recovery value on the recovery knob (1506)        may be associated with a particular recovery time objective        (‘RTO’) that specifies how long it will take the storage to        restore from an incident that causes the storage to become        unavailable, a particular recovery value on the recovery knob        (1506) may be associated with a particular recovery point        objective (‘RPO’) that limits how far the storage can be rolled        back in time after a failure incident and defines the maximum        allowable amount of lost data measured in time from a failure        occurrence to the last valid backup, and so on. In such an        example, the user may be able to view the various        recovery-related metrics and guarantees that can be delivered at        each value on the recovery knob (1506) in a variety of ways. For        example, hovering a mouse pointer over a particular value (e.g.,        ‘6’) on the recovery knob (1506) may cause a pop-up to be        generated that contains information describing the various        recovery-related metrics and guarantees that can be delivered at        that particular value. Alternatively, a second window may exist        to display information describing the various recovery-related        metrics and guarantees that can be delivered at the currently        selected value on the recovery knob (1506). Readers will        appreciate that a secondary window, pop-up, or other medium for        conveying information to the user can include additional        information, including the costs associated with a particular        value on the recovery knob (1506).    -   c. A security knob (1508) that enables a user to select how        secure that data associated with a particular application should        be. In such an example, a particular value on the security knob        (1508) can be associated with how heavily the storage should        encrypt data that is associated with a particular application, a        particular value on the security knob (1508) can be associated        with a particular encryption algorithm that the storage should        encrypt data that is associated with a particular application, a        particular value on the security knob (1508) can be associated        with the extent to which data that is associated with a        particular application should be stored in a way that is General        Data Protection Regulation (‘GDPR’) compliant, and so on. In        such an example, the user may be able to view the various        security-related guarantees that can be delivered at each value        on the security knob (1508) in a variety of ways. For example,        hovering a mouse pointer over a particular value (e.g., ‘6’) on        the security knob (1508) may cause a pop-up to be generated that        contains information describing the various security-related        guarantees that can be delivered at that particular value on the        security knob (1508). Alternatively, a second window may exist        to display information describing the various security-related        guarantees that can be delivered at the currently selected value        on the security knob (1508). Readers will appreciate that a        secondary window, pop-up, or other medium for conveying        information to the user can include additional information,        including the costs associated with a particular value on the        security knob (1508).    -   d. A location knob (1510) that enables a user to select where        data associated with a particular application should be kept. In        such an example, a particular value on the location knob (1510)        can be associated with a requirement that data that is        associated with a particular application should be kept within a        predefined proximity (e.g., on the same network) relative to the        location where the application is executing, a particular value        on the location knob (1510) can be associated with a requirement        that data that is associated with a particular application        should be kept on a location that meets certain predefined        criteria (e.g., behind a firewall, on a non-public cloud, on a        public cloud), and so on. In such an example, the user may be        able to view the various location-related guarantees that can be        delivered at each value on the location knob (1510) in a variety        of ways. For example, hovering a mouse pointer over a particular        value (e.g., ‘6’) on the location knob (1510) may cause a pop-up        to be generated that contains information describing the various        location-related guarantees that can be delivered at that        particular value on the location knob (1510). Alternatively, a        second window may exist to display information describing the        various location-related guarantees that can be delivered at the        currently selected value on the location knob (1510). Readers        will appreciate that a secondary window, pop-up, or other medium        for conveying information to the user can include additional        information, including the costs associated with a particular        value on the location knob (1510).

Readers will appreciate that the GUI (1502) depicted in FIG. 15 in onlyone example of a GUI that may be provided in accordance with embodimentsof the present disclosure. In other embodiments, such a GUI can includeadditional or fewer knobs that relate to additional or fewercharacteristics of storage that is to be provided to a particularapplication. For example, one additional knob that may be of particularinterest when tuning cloud-based storage systems is a ‘cost’ knob.Readers will appreciate that in constructing a cloud-based storagesystem, some of the underlying resources (e.g., EC2 instances thatsupport the execution of the storage controller application, EC2instances with local storage that can be used as virtual drives, S3buckets, networking resources used to support communications betweenvarious components, and many other) may be billed based on usage. Forexample, Amazon may charge for the usage of such resources in AWS,Microsoft may charge for the usage of such resources in Azure, and soon. As such, a ‘cost’ knob may be used to specify the extent to whichcost considerations should be taken into consideration. For example, ifthe ‘cost’ knob is set to a value such that controlling costs iscritical, steps may be taken to control costs. Examples of steps thatmay be taken to control costs can include, for example, executing thestorage controller application on a relatively low-powered (i.e. cheap)EC2 instance type, having only a single EC2 instance that is executingthe storage controller application thereby increasing the amount of timethat is required to fail over to a new EC2 instance, having the datasetonly partially mirrored in the virtual drive layer rather than havingthe entire dataset available in the virtual drive layer, and so on.Alternatively, if the ‘cost’ knob is set to a value such thatcontrolling costs is not important, steps may be taken to guaranteehigher levels of performance, durability, and the like—regardless of theassociated financial costs.

Readers will further appreciate that such GUIs enable a user to selectvarious characteristics of storage that should be made accessible to aparticular application, while the implementation details associated withproviding such storage are handled automatically and without userintervention. In such an example, storage systems such as thosedescribed above may provide the actual storage to the application. Thestorage systems may reside, for example, in a data center, in a cloudcomputing environment as described above, or other location and mayinclude storage systems that provide block storage, storage systems thatprovide file storage, storage systems that provide object storage, orany combination thereof. Furthermore, the storage systems may bescale-up storage systems, scale-out storage systems, or any combinationthereof. In such an example, the user's manipulation of the GUI maycause the application to be moved within the data center in order toobtain the desired characteristics of the storage that is accessible tothe application, the application may mount a volume that is givensufficient resources to deliver the desired characteristics of thestorage that is accessible to the application, a replication policy orbackup policy may be attached to a volume that is used as storage for aparticular application in order to achieve backup or availabilityobjectives for the storage that is accessible to the application, and soon. Stated more generally, the application, storage system, networkingresources, storage resources, or other resources may be configured inresponse to a user's manipulation of the GUI in order to provide storageto the application in accordance with the desired storagecharacteristics as expressed via the GUI.

In some embodiments of the present disclosure, an infrastructurediscovery service may be utilized to determine the particularcapabilities and resources that are offered by a particular system, suchas one of the storage systems described above. Such an infrastructurediscovery service may be embodied, for example, as one or more modulesof computer program instructions that are executing on computerhardware, including virtualized execution environments such as a virtualmachine, a container, and so on. The infrastructure discovery servicemay be configured to take an inventory of a particular system todiscover the hardware components contained therein, the softwarecomponents contained therein, the networking components containedtherein, and so on. In such a way, the infrastructure discovery servicecan detect what resources and capabilities that a particular system canprovide, such that a preliminary determination can be made as to whethera particular system has the necessary resources to support a particularapplication in accordance with its characteristics.

Although some example embodiments are described in a way where a seriesof steps appear to be occurring in a particular order, no such orderingis required unless explicitly stated. Likewise, steps that are depictedin one figure may be combined with steps described in another figure,even if the respective steps are not depicted in a single figure.Readers will further appreciate that although various embodiments may bedescribed separately from each other, combinations of multipleembodiments are also within the scope of the present disclosure unlessexplicitly prohibited.

In view of the descriptions contained above, embodiments describedherein can provide for many benefits, including at least the following:

-   -   Applications can be supported by storage systems that are        intelligently tailored to supply the appropriate level of        support to the application.    -   Storage resources can be efficiently utilized to avoid wasting        resources, oversubscribing resources, and the like.

Example embodiments are described largely in the context of a fullyfunctional computer system. Readers of skill in the art will recognize,however, that the present disclosure also may be embodied in a computerprogram product disposed upon computer readable storage media for usewith any suitable data processing system. Such computer readable storagemedia may be any storage medium for machine-readable information,including magnetic media, optical media, or other suitable media.Examples of such media include magnetic disks in hard drives ordiskettes, compact disks for optical drives, magnetic tape, and othersas will occur to those of skill in the art. Persons skilled in the artwill immediately recognize that any computer system having suitableprogramming means will be capable of executing the steps of the methodas embodied in a computer program product. Persons skilled in the artwill recognize also that, although some of the example embodimentsdescribed in this specification are oriented to software installed andexecuting on computer hardware, nevertheless, alternative embodimentsimplemented as firmware or as hardware are well within the scope of thepresent disclosure.

Embodiments can include be a system, a method, and/or a computer programproduct. The computer program product may include a computer readablestorage medium (or media) having computer readable program instructionsthereon for causing a processor to carry out aspects of the presentdisclosure.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to some embodimentsof the disclosure. It will be understood that each block of theflowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer readable instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

What is claimed is:
 1. A method comprising: identifying, for anapplication that utilizes storage resources within a cloud-based storagesystem, one or more storage performance characteristics associated withthe application; comparing the storage performance characteristics ofthe application that were identified with storage performancecharacteristics of storage resources of one or more cloud-based storagesystems; and selecting, based on the comparing, one or more storageresources within the one or more cloud-based storage systems to providestorage services to the application.
 2. The method of claim 1 whereinselecting, in dependence upon the one or more storage performancecharacteristics associated with the application and storage performancecharacteristics of storage resources within one or more cloud-basedstorage systems, one or more storage resources within the one or morecloud-based storage systems to support the execution of the applicationfurther comprises selecting, from available storage resources that couldbe included in a cloud-based storage system, a subset of storageresources that can be provided by the cloud computing environment forinclusion within the cloud-based storage system that is to be created tosupport the execution of the application.
 3. The method of claim 1wherein selecting, in dependence upon the one or more storageperformance characteristics associated with the application and storageperformance characteristics of storage resources within one or morecloud-based storage systems, one or more storage resources within theone or more cloud-based storage systems to support the execution of theapplication further comprises selecting, from amongst a plurality ofcloud-based storage systems that are supported by the cloud computingenvironment, a particular cloud-based storage system to support theexecution of the application.
 4. The method of claim 1 whereinselecting, in dependence upon the one or more storage performancecharacteristics associated with the application and storage performancecharacteristics of storage resources within one or more cloud-basedstorage systems, one or more storage resources within the one or morecloud-based storage systems to support the execution of the applicationfurther comprises selecting, from amongst a plurality of storage systemvirtual controller compute instances, a particular set of one or morestorage system virtual controller compute instances to service I/Ooperations generated by the application.
 5. The method of claim 1further comprising: identifying, in dependence upon a predicted changeto the application, one or more updated storage performancecharacteristics associated with the application, wherein the predictedchange comprises a predicted increase in required processing storageresources to support the application; and selecting, in dependence uponthe one or more updated storage performance characteristics associatedwith the application and storage performance characteristics of one ormore storage resources within one or more of the cloud-based storagesystems, an updated set of storage resources within the one or morecloud-based storage systems to support the execution of the application.6. The method of claim 1 further comprising: identifying, in dependenceupon a detected change to the application, one or more updated storageperformance characteristics associated with the application; andselecting, in dependence upon the one or more updated storageperformance characteristics associated with the application and storageperformance characteristics of one or more storage resources within oneor more of the cloud-based storage systems, an updated set of storageresources within the one or more cloud-based storage systems to supportthe execution of the application.
 7. The method of claim 6 wherein theapplication utilizes the updated set of storage resources within the oneor more cloud-based storage systems without migrating any portion of thedataset that is stored as objects within object storage resources in thecloud-based storage system.
 8. The method of claim 6 wherein selectingan updated set of storage resources within the one or more cloud-basedstorage systems to support the execution of the application furthercomprises modifying storage resources within the one or more cloud-basedstorage systems.
 9. The method of claim 8 wherein modifying storageresources within the one or more cloud-based storage systems furthercomprises modifying a virtual drive layer in at least one of thecloud-based storage systems.
 10. The method of claim 8 wherein modifyingstorage resources within the one or more cloud-based storage systemsfurther comprises modifying a storage controller layer in at least oneof the cloud-based storage systems.
 11. The method of claim 8, furthercomprising: receiving, via a storage tuning interface, tuninginformation for a cloud-based storage system that is utilized by theapplication; and wherein modifying storage resources within the one ormore cloud-based storage systems is carried out in response to receivingthe tuning information.
 12. The method of claim 1 further comprising:detecting a change to one or more of the cloud-based storage systems;identifying, in dependence upon the detected change to one or more ofthe cloud-based storage systems, one or more updated storage performancecharacteristics associated with the one or more cloud-based storagesystems; and selecting, in dependence upon the storage performancecharacteristics associated with the application and the updated storageperformance characteristics associated with the one or more cloud-basedstorage systems, an updated set of storage resources within the one ormore cloud-based storage systems to support the execution of theapplication.
 13. The method of claim 1 further comprising: detectingthat one or more storage resources within the cloud-based storage systemhave become constrained; and automatically, without user intervention,performing corrective actions.
 14. A method comprising: identifying, foran application that utilizes storage resources within a cloud-basedstorage system, one or more storage performance characteristicsassociated with the application; comparing the storage performancecharacteristics of the application that were identified with storageperformance characteristics of storage resources of one or morecloud-based storage systems; and selecting, based on the comparing, oneor more storage resources within the one or more cloud-based storagesystems to provide storage services to the application.
 15. The methodof claim 14 wherein: for each application of a plurality of applicationssupported by the storage system, the storage resources within thestorage system that support the execution of the application are locatedin distinct application isolation domains.
 16. The method of claim 14further comprising generating a recommendation to cease supporting theexecution of the application on first storage resources within thestorage system and begin supporting the execution of the application onsecond storage resources within the storage system.
 17. The method ofclaim 14 further comprising: identifying, in dependence upon predictedchange to the application, one or more updated storage performancecharacteristics associated with the application, wherein the predictedchange comprises a predicted increase in required processing storageresources to support the application; and selecting, in dependence uponthe one or more updated storage performance characteristics associatedwith the application and storage performance characteristics of storageresources within the storage system, an updated set of storage resourceswithin the storage system to support the execution of the application.18. The method of claim 14 further comprising: selecting, in dependenceupon the one or more storage performance characteristics associated withthe application and updated storage performance characteristics ofstorage resources within the storage system, an updated set of storageresources within the storage system to support the execution of theapplication.
 19. The method of claim 14 further comprising: performingcorrective actions in response to detecting that one or more storageresources within the storage system will become constrained.
 20. Anapparatus comprising a computer processor, a computer memory operativelycoupled to the computer processor, the computer memory having disposedwithin it computer program instructions that, when executed, cause theapparatus to carry out the steps of: identifying, for an applicationthat utilizes storage resources within a cloud-based storage system, oneor more storage performance characteristics associated with theapplication; comparing the storage performance characteristics of theapplication that were identified with storage performancecharacteristics of storage resources of one or more cloud-based storagesystems; and selecting, based on the comparing, one or more storageresources within the one or more cloud-based storage systems to providestorage services to the application.